{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":76,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":76,"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":"01fbf02107a4","filters":{"venue":"Electronic Journal of Statistics"}},"results":[{"id":"W1996851858","doi":"10.1214/14-ejs920","title":"Dynamic treatment regimes: Technical challenges and applications","year":2014,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":170,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Drug Abuse; University of Michigan; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Estimator; Operationalization; Inference; Mathematics; Variety (cybernetics); Decision rule; Machine learning; Econometrics; Computer science; Mathematical optimization; Artificial intelligence; Statistics","authors":[{"name":"Eric B. Laber","is_ca":false},{"name":"Daniel J. Lizotte","is_ca":true},{"name":"Min Qian","is_ca":false},{"name":"William E. Pelham","is_ca":false},{"name":"Susan A. Murphy","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0495202221317277,"gpt":0.3737772155890598,"spread":0.3242569934573321,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03714244,0.0008312976,0.002245482,0.001697129,0.001060825,0.004278952,0.00374983,0.003652813,0.005116534],"category_scores_gemma":[0.1677909,0.001155075,0.001425983,0.002190823,0.005292621,0.005389266,0.003421041,0.009174,0.000760501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003159452,"about_ca_system_score_gemma":0.003128234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003420493,"about_ca_topic_score_gemma":0.001741117,"domain_scores_codex":[0.9785847,0.01460016,0.001076402,0.002621176,0.002788639,0.0003289164],"domain_scores_gemma":[0.8613757,0.1220825,0.004783264,0.008195692,0.002818032,0.0007448696],"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":[0.0001420134,0.0001132964,0.003412907,0.0004954879,0.0001379209,0.0001785036,0.0003702368,0.05794464,0.0004634971,0.793937,0.003758801,0.1390457],"study_design_scores_gemma":[0.00005636011,0.00006446848,0.0006239272,0.0002064971,0.00003002128,0.000139032,0.00009546923,0.1092546,0.0002685203,0.8805463,0.008672597,0.00004218199],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004633817,0.004885356,0.9764277,0.00875691,0.0002042902,0.0002099093,0.0003266008,0.000153847,0.004401549],"genre_scores_gemma":[0.25666,0.008910781,0.7251828,0.003048539,0.001177202,0.001936693,0.0004392764,0.0001877468,0.002457056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03714244,"threshold_uncertainty_score":0.1964303,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097769384","doi":"10.1214/11-ejs606","title":"A weighted k-nearest neighbor density estimate for geometric inference","year":2011,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":93,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Centre National de la Recherche Scientifique; Fonds Québécois de la Recherche sur la Nature et les Technologies; Agence Nationale de la Recherche; Natural Sciences and Engineering Research Council of Canada; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Pointwise; Mathematics; k-nearest neighbors algorithm; Inference; Consistency (knowledge bases); Range (aeronautics); Limit (mathematics); Algorithm; Applied mathematics; Discrete mathematics; Artificial intelligence; Computer science; Mathematical analysis","authors":[{"name":"Gérard Biau","is_ca":false},{"name":"Frédéric Chazal","is_ca":false},{"name":"David Cohen‐Steiner","is_ca":false},{"name":"Luc Devroye","is_ca":true},{"name":"Carlos C. Rodríguez","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09590211664165836,"gpt":0.3684236437901869,"spread":0.2725215271485286,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005853903,0.0007157482,0.00131476,0.002374384,0.0007439453,0.001602287,0.003090857,0.001810781,0.002221928],"category_scores_gemma":[0.03110063,0.0006434688,0.00124722,0.002164054,0.001993793,0.004100488,0.002814238,0.002596466,0.0008087819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004506,"about_ca_system_score_gemma":0.001128773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002177287,"about_ca_topic_score_gemma":0.00175262,"domain_scores_codex":[0.9962608,0.001614084,0.0001769049,0.0006607338,0.001162274,0.0001251232],"domain_scores_gemma":[0.9928713,0.004215464,0.000520041,0.001090254,0.001144005,0.0001588199],"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":[0.0001386649,0.00008326159,0.002507681,0.0004459276,0.0002411159,0.0001600359,0.000171223,0.2665103,0.004826985,0.5686955,0.003646902,0.1525724],"study_design_scores_gemma":[0.00001467329,0.00004547588,0.0005301316,0.00004391842,0.00003506889,0.0001360308,0.00002138709,0.8182461,0.001131911,0.1764387,0.003319273,0.00003729058],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001928639,0.00018776,0.9973345,0.00007629403,0.00002967012,0.0000152971,0.00004138271,0.00004493893,0.0003414756],"genre_scores_gemma":[0.1386766,0.0009357195,0.856367,0.0002514161,0.0002920846,0.0002819287,0.0006170459,0.0001563456,0.002421913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005853903,"threshold_uncertainty_score":0.03095877,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2018776504","doi":"10.1214/13-ejs866","title":"Statistical testing of covariate effects in conditional copula models","year":2013,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":47,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Covariate; Mathematics; Parametric statistics; Econometrics; Inference; Conditional probability distribution; Statistical inference; Statistics; Statistical hypothesis testing; Null hypothesis; Applied mathematics; Computer science; Artificial intelligence","authors":[{"name":"Elif F. Acar","is_ca":true},{"name":"Radu V. Craiu","is_ca":true},{"name":"Fang Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03503117493124985,"gpt":0.2372490505781863,"spread":0.2022178756469364,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04107781,0.001183466,0.001904462,0.001605958,0.0008055173,0.002337282,0.003094289,0.002411165,0.003881052],"category_scores_gemma":[0.2747668,0.0007041271,0.002001839,0.002434967,0.003695136,0.004774062,0.003135536,0.002602903,0.0004757395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008657896,"about_ca_system_score_gemma":0.001597942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001517826,"about_ca_topic_score_gemma":0.00066917,"domain_scores_codex":[0.959916,0.03128315,0.001001895,0.004273944,0.002759147,0.000765869],"domain_scores_gemma":[0.6399788,0.3256326,0.01125848,0.0178685,0.003963385,0.001298206],"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":[0.001972231,0.000696448,0.1151612,0.0009059553,0.00243299,0.00205301,0.00206395,0.2586227,0.009406149,0.3890474,0.003772255,0.2138657],"study_design_scores_gemma":[0.0001540474,0.0008831593,0.0322773,0.00008791178,0.0002405325,0.0004689757,0.0002397412,0.74134,0.004602951,0.2174651,0.002121209,0.0001190951],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1494238,0.0002773181,0.8475553,0.0002959761,0.00003250875,0.0001542745,0.0004346929,0.0005300888,0.001296037],"genre_scores_gemma":[0.9029464,0.0001940736,0.0948787,0.0001447369,0.00005814357,0.00045133,0.0007118687,0.0001479538,0.0004667796],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04107781,"threshold_uncertainty_score":0.2172428,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2079400333","doi":"10.1214/12-ejs692","title":"A nonparametric multivariate multisample test based on data depth","year":2012,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":47,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Mahalanobis distance; Nonparametric statistics; Statistics; Test statistic; Null distribution; Multivariate statistics; Statistical hypothesis testing; Null hypothesis; Statistic; Type I and type II errors; Ranking (information retrieval); Chi-square test; Artificial intelligence","authors":[{"name":"Shojaeddin Chenouri","is_ca":true},{"name":"Christopher G. Small","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1574923567074898,"gpt":0.4431857527929468,"spread":0.285693396085457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01056009,0.0007437762,0.00149166,0.003434748,0.0009376598,0.001879431,0.00169391,0.001348313,0.006115604],"category_scores_gemma":[0.07808915,0.0002859963,0.001165741,0.002605124,0.002494041,0.003747134,0.002766197,0.002320624,0.00107672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007227052,"about_ca_system_score_gemma":0.00147177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005901994,"about_ca_topic_score_gemma":0.0004776922,"domain_scores_codex":[0.9866882,0.006268216,0.0006376241,0.001738588,0.004266608,0.0004006783],"domain_scores_gemma":[0.9410383,0.04151063,0.005200664,0.005218838,0.005806808,0.001224742],"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":[0.001334094,0.0004821016,0.04926402,0.0007920125,0.0004800892,0.0004715768,0.001095288,0.04127218,0.01866041,0.1204462,0.007937596,0.7577645],"study_design_scores_gemma":[0.0004296785,0.003927203,0.08136751,0.0003668196,0.0002716301,0.002894993,0.001401859,0.6306221,0.03206659,0.2103557,0.03568994,0.0006059699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03788154,0.0002181344,0.9568274,0.0002963916,0.0001476771,0.0002313162,0.0004412646,0.0007865945,0.003169696],"genre_scores_gemma":[0.5038642,0.0002187182,0.4909914,0.0004539818,0.0003224329,0.001106472,0.001034934,0.0003261527,0.001681566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01056009,"threshold_uncertainty_score":0.0558477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2000045933","doi":"10.1214/11-ejs594","title":"A Metropolis-Hastings based method for sampling from the G-Wishart distribution in Gaussian graphical models","year":2011,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Wishart distribution; Mathematics; Metropolis–Hastings algorithm; Gaussian; Deviance (statistics); Graphical model; Conjugate prior; Sampling (signal processing); Statistics; Applied mathematics; Algorithm; Prior probability; Computer science; Markov chain Monte Carlo; Bayesian probability; Multivariate statistics","authors":[{"name":"Nicholas Mitsakakis","is_ca":true},{"name":"Hélène Massam","is_ca":true},{"name":"Michael Escobar","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.101723487055519,"gpt":0.3771730557496278,"spread":0.2754495686941087,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005253581,0.001029058,0.001227146,0.001341746,0.0009770915,0.001131109,0.003037786,0.001443186,0.002607156],"category_scores_gemma":[0.01522669,0.0009077789,0.001418188,0.001788751,0.002574544,0.002409704,0.001426645,0.003152698,0.0009446794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109635,"about_ca_system_score_gemma":0.001417754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003875855,"about_ca_topic_score_gemma":0.005083343,"domain_scores_codex":[0.9972747,0.001559324,0.0001107039,0.0003452401,0.0006155398,0.00009452736],"domain_scores_gemma":[0.9941672,0.004212309,0.0002691146,0.0007366181,0.0004803827,0.0001344228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003358115,0.0001883241,0.002725339,0.0003743775,0.000312817,0.0003797259,0.000483751,0.3182641,0.008812643,0.471162,0.005004164,0.1919569],"study_design_scores_gemma":[0.00006655653,0.00007946372,0.0003810336,0.00002791454,0.00003876145,0.0001317339,0.00001932883,0.8837683,0.003554738,0.1088418,0.003023734,0.00006668176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001482534,0.00007791394,0.9979717,0.00004007787,0.00001654045,0.00003706417,0.00001693531,0.0001779085,0.0001793719],"genre_scores_gemma":[0.0729252,0.0003162187,0.9239768,0.0001730556,0.00009030824,0.0004183562,0.000205168,0.0002677036,0.001627144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005253581,"threshold_uncertainty_score":0.02778393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057741899","doi":"10.1214/09-ejs526","title":"Estimation of a discrete monotone distribution","year":2009,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":39,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Estimator; Monotone polygon; Consistent estimator; Applied mathematics; Statistics; Efficient estimator; Distribution (mathematics); Asymptotic distribution; Empirical distribution function; Trimmed estimator; Minimum-variance unbiased estimator; Combinatorics; Mathematical analysis","authors":[{"name":"Hanna Jankowski","is_ca":true},{"name":"Jon A. Wellner","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02781778922533597,"gpt":0.3537055251171534,"spread":0.3258877358918175,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01476157,0.0005552544,0.0008736386,0.001526957,0.0003512187,0.001391693,0.001930331,0.001174021,0.002810854],"category_scores_gemma":[0.08886024,0.0003312771,0.0005861589,0.0007818458,0.002105793,0.00223418,0.002297876,0.001623611,0.0004135325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008801537,"about_ca_system_score_gemma":0.0009497807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007032242,"about_ca_topic_score_gemma":0.0004370437,"domain_scores_codex":[0.9917594,0.005324795,0.0001981508,0.0007920718,0.001702404,0.0002232416],"domain_scores_gemma":[0.9407029,0.04903092,0.004210531,0.003191016,0.002373889,0.0004906819],"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":[0.000592267,0.0001815499,0.03194558,0.0008691211,0.0004129791,0.000456973,0.0005350597,0.2094064,0.009515302,0.4696409,0.003569287,0.2728746],"study_design_scores_gemma":[0.0001240573,0.000289694,0.005533491,0.0001198102,0.000052481,0.0006393397,0.0001041753,0.8289243,0.003321309,0.1571764,0.003664196,0.00005074182],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05463091,0.0004042886,0.9417458,0.0005534727,0.00003402433,0.00007074758,0.00009132709,0.000176094,0.002293443],"genre_scores_gemma":[0.6530961,0.000451906,0.3435133,0.0003217387,0.0001489761,0.0002410658,0.0004548411,0.0001086192,0.00166335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01476157,"threshold_uncertainty_score":0.07806754,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2019774049","doi":"10.1214/11-ejs615","title":"Estimation and detection of functions from anisotropic Sobolev classes","year":2011,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Russian Foundation for Basic Research","keywords":"Mathematics; Minimax; Sobolev space; Estimator; Applied mathematics; Minimax estimator; White noise; Function (biology); Asymptotically optimal algorithm; Gaussian; Gaussian noise; Connection (principal bundle); Mathematical optimization; Mathematical analysis; Statistics; Algorithm; Minimum-variance unbiased estimator","authors":[{"name":"Yuri I. Ingster","is_ca":true},{"name":"Natalia Stepanova","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03514849318215581,"gpt":0.2746813717635521,"spread":0.2395328785813963,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004288247,0.001114746,0.001342921,0.00174312,0.0003506015,0.001278141,0.00129317,0.001506794,0.0004198566],"category_scores_gemma":[0.02100957,0.0005868152,0.0007328357,0.0009567724,0.002304196,0.002091622,0.002258277,0.001608274,0.00011597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006201456,"about_ca_system_score_gemma":0.00043189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009923687,"about_ca_topic_score_gemma":0.0003027554,"domain_scores_codex":[0.9985544,0.0006139539,0.00007623852,0.0002365719,0.0003857048,0.0001331522],"domain_scores_gemma":[0.9909465,0.006300361,0.001306818,0.0005350133,0.0006649658,0.0002462501],"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":[0.0004828747,0.00009684831,0.009030713,0.0004634026,0.0001532452,0.0008241297,0.0005672125,0.4188287,0.03114212,0.4368941,0.001140841,0.1003758],"study_design_scores_gemma":[0.000008777698,0.00004356015,0.001045689,0.00001995229,0.00001535163,0.0001825547,0.00004082953,0.9041995,0.004274191,0.08949731,0.0006407018,0.00003157768],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07449177,0.0004483141,0.9238155,0.0002068263,0.00002098553,0.00002145825,0.0000345681,0.00005482683,0.0009057058],"genre_scores_gemma":[0.8078579,0.0009933499,0.1884888,0.0000980172,0.0001531237,0.0001115753,0.0002060833,0.00007566782,0.002015498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004288247,"threshold_uncertainty_score":0.02267867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2974112908","doi":"10.1214/22-ejs2001","title":"Minimax confidence intervals for the Sliced Wasserstein distance","year":2022,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Confidence distribution; Minimax; Frequentist inference; Coverage probability; CDF-based nonparametric confidence interval; Confidence interval; Wasserstein metric; Inference; Statistics; Robust confidence intervals; Probability distribution; Mathematical optimization; Bayesian probability; Applied mathematics; Bayesian inference; Artificial intelligence; Computer science","authors":[{"name":"Tudor Manole","is_ca":false},{"name":"Sivaraman Balakrishnan","is_ca":false},{"name":"Larry Wasserman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04746687519815738,"gpt":0.355985738267535,"spread":0.3085188630693776,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02056533,0.001413645,0.002154194,0.0031669,0.0008890214,0.003727349,0.003949458,0.002538995,0.003914201],"category_scores_gemma":[0.1926586,0.001005205,0.001633071,0.002125887,0.00507835,0.007926504,0.005794233,0.005936396,0.000534256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002230551,"about_ca_system_score_gemma":0.001536268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001253046,"about_ca_topic_score_gemma":0.0007606721,"domain_scores_codex":[0.9891397,0.005218477,0.0005944677,0.001964408,0.002697885,0.0003849792],"domain_scores_gemma":[0.766269,0.2041351,0.00995689,0.01053369,0.007302388,0.001802881],"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":[0.0001926463,0.00005481384,0.003105384,0.0002993263,0.0001286058,0.0001369922,0.0002997186,0.2864825,0.001400109,0.6722969,0.001162398,0.03444063],"study_design_scores_gemma":[0.00002559278,0.00008299481,0.0006324154,0.0001406183,0.0000235939,0.0001138412,0.00003486538,0.6747779,0.00122977,0.3217445,0.001145894,0.00004803911],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008840754,0.0005153219,0.9891993,0.0003004654,0.00002899071,0.00002879508,0.0001015224,0.0001186707,0.0008661308],"genre_scores_gemma":[0.5879441,0.001692508,0.4062633,0.00047108,0.0003756642,0.0005213569,0.0007180831,0.0003219553,0.001691866],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02056533,"threshold_uncertainty_score":0.1087612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2964133571","doi":"10.1214/17-ejs1313","title":"Quantile processes for semi and nonparametric regression","year":2017,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Quantile; Mathematics; Estimator; Nonparametric statistics; Conditional probability distribution; Series (stratigraphy); Quantile regression; Inference; Applied mathematics; Parametric statistics; Dimension (graph theory); Statistical inference; Econometrics; Statistics; Computer science","authors":[{"name":"Shih-Kang Chao","is_ca":true},{"name":"Stanislav Volgushev","is_ca":true},{"name":"Guang Cheng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0816673518411835,"gpt":0.4095705847868205,"spread":0.327903232945637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007993898,0.001162597,0.00146709,0.002769039,0.000807137,0.002925085,0.001732968,0.002365361,0.005656464],"category_scores_gemma":[0.02407757,0.0006762426,0.001465171,0.003598436,0.003680587,0.003902383,0.002841267,0.005310122,0.001409284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002339298,"about_ca_system_score_gemma":0.001466972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002678108,"about_ca_topic_score_gemma":0.001157448,"domain_scores_codex":[0.9969394,0.001556949,0.0001406328,0.0004027766,0.0007970872,0.0001631607],"domain_scores_gemma":[0.9906085,0.006387558,0.0007568024,0.001055724,0.0008883215,0.0003031489],"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":[0.00000886364,0.000009304362,0.0002033261,0.00005630852,0.00001600693,0.00003399187,0.00007738783,0.01134266,0.00026389,0.9774506,0.000848669,0.009688917],"study_design_scores_gemma":[0.00000786948,0.00001371084,0.0002395999,0.00003826888,0.000009060938,0.00004667609,0.00002751338,0.1520596,0.0001561862,0.8417835,0.005605395,0.00001266274],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004353373,0.003697787,0.985693,0.0009992807,0.0001327987,0.00002678311,0.0001713786,0.0001646613,0.004760968],"genre_scores_gemma":[0.4987765,0.01688037,0.4578724,0.001277595,0.002592396,0.0008070998,0.001184507,0.0005892852,0.02001983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007993898,"threshold_uncertainty_score":0.04227626,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963401972","doi":"10.1214/17-ejs1325","title":"Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models","year":2017,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Estimator; Autoregressive model; Smoothing; Rate of convergence; Smoothness; Applied mathematics; Matrix (chemical analysis); Mathematical optimization; Statistics; Mathematical analysis","authors":[{"name":"Xin Ding","is_ca":true},{"name":"Ziyi Qiu","is_ca":false},{"name":"Xiaohui Chen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01150901946578509,"gpt":0.2547185093057985,"spread":0.2432094898400134,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001518688,0.0005985889,0.0007275562,0.0005415556,0.0002417951,0.0006537187,0.0008896637,0.0008681163,0.0008776995],"category_scores_gemma":[0.008076365,0.0003571594,0.0006748385,0.0007484683,0.0008346261,0.001116411,0.0008460444,0.001671368,0.0002530862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003248407,"about_ca_system_score_gemma":0.0005764164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00236174,"about_ca_topic_score_gemma":0.002404573,"domain_scores_codex":[0.9993992,0.0002310676,0.00002935295,0.0001738204,0.0001202185,0.00004635729],"domain_scores_gemma":[0.9962775,0.002725371,0.0004274815,0.0002915977,0.0002170619,0.00006090225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009547633,0.00006150147,0.002483378,0.0001388848,0.00009260637,0.0001531994,0.000112216,0.863394,0.007887529,0.04793461,0.0008834932,0.07676303],"study_design_scores_gemma":[0.000002963684,0.00001339943,0.0002296328,0.000003549221,0.000005117156,0.00001628901,0.000005206024,0.9908838,0.0005900956,0.008084634,0.0001594787,0.000005909796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01282322,0.00009524266,0.9866698,0.00009369044,0.00001126072,0.00000836136,0.00003718093,0.00009359176,0.0001676676],"genre_scores_gemma":[0.678532,0.0008301041,0.3165654,0.000175369,0.00017163,0.0001229877,0.0006515549,0.0000959926,0.002854897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00236174,"threshold_uncertainty_score":0.008031666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392404692","doi":"10.1214/24-ejs2217","title":"Limit theorems for entropic optimal transport maps and Sinkhorn divergence","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Mathematical Dynamics and Fractals","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Limit (mathematics); Divergence (linguistics); Kullback–Leibler divergence; Applied mathematics; Statistical physics; Mathematical economics; Combinatorics; Mathematical analysis; Statistics; Physics","authors":[{"name":"Ziv Goldfeld","is_ca":false},{"name":"Kengo Kato","is_ca":false},{"name":"Gabriel Rioux","is_ca":false},{"name":"Ritwik Sadhu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01615217744983037,"gpt":0.2863996864545678,"spread":0.2702475090047374,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007885524,0.001039917,0.001113315,0.003940882,0.001365248,0.002498368,0.002074729,0.001605229,0.005884247],"category_scores_gemma":[0.0424964,0.0005385025,0.001872942,0.001176027,0.005368396,0.008264986,0.003953223,0.003005865,0.0006326658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002297007,"about_ca_system_score_gemma":0.00122728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001311352,"about_ca_topic_score_gemma":0.0007442471,"domain_scores_codex":[0.9985003,0.0005886846,0.00009437269,0.0002626831,0.0003618248,0.0001920836],"domain_scores_gemma":[0.9755245,0.01674271,0.002117653,0.00121069,0.002992671,0.001411865],"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":[0.00002209104,0.00002667559,0.001054573,0.00008080267,0.00002249032,0.0001545427,0.0001311005,0.01016816,0.001051705,0.9828956,0.0005969775,0.003795146],"study_design_scores_gemma":[0.00001298079,0.00003739888,0.0007112984,0.00005054835,0.00001330221,0.0002356086,0.00008827112,0.16255,0.000952925,0.8343248,0.0009938707,0.00002896597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1360062,0.001387521,0.8437667,0.001809404,0.000129258,0.00009738858,0.0002668034,0.0002831531,0.01625343],"genre_scores_gemma":[0.8850944,0.00187171,0.097266,0.0007846396,0.0004012025,0.0004859558,0.0005487539,0.0004967349,0.01305061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007885524,"threshold_uncertainty_score":0.04170316,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2952566994","doi":"10.1214/19-ejs1566","title":"Empirical likelihood inference for non-randomized pretest-posttest studies with missing data","year":2019,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Empirical likelihood; Mathematics; Statistics; Confidence interval; Wald test; Missing data; Score test; Inference; Propensity score matching; Randomized experiment; Statistic; Coverage probability; Likelihood-ratio test; Test statistic; Restricted maximum likelihood; Statistical hypothesis testing; Econometrics; Maximum likelihood; Artificial intelligence; Computer science","authors":[{"name":"Shixiao Zhang","is_ca":true},{"name":"Peisong Han","is_ca":false},{"name":"Changbao Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1420432733550241,"gpt":0.4623509266687351,"spread":0.320307653313711,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1103455,0.001778665,0.004341978,0.004527866,0.001045285,0.003339529,0.006127092,0.00340177,0.01070102],"category_scores_gemma":[0.4093603,0.001554994,0.003358074,0.004110874,0.003793819,0.005213647,0.003544563,0.004494224,0.001117335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001745699,"about_ca_system_score_gemma":0.003118732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972243,"about_ca_topic_score_gemma":0.00132481,"domain_scores_codex":[0.9388063,0.05169942,0.002249985,0.003283019,0.003410793,0.0005504045],"domain_scores_gemma":[0.5203869,0.4442106,0.01428292,0.01617774,0.004117675,0.0008240892],"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":[0.0009013703,0.0003468679,0.01764976,0.002435794,0.001751863,0.001242851,0.0009201024,0.1129869,0.0008420289,0.6161676,0.005333388,0.2394215],"study_design_scores_gemma":[0.0004048328,0.0002584082,0.00195189,0.0004354795,0.0003518689,0.0003231455,0.0001430446,0.3655489,0.001232974,0.6253164,0.003976828,0.00005613258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002830345,0.0005209382,0.9950758,0.0003612977,0.00004624447,0.0002182846,0.000159586,0.0002135463,0.000574033],"genre_scores_gemma":[0.2254801,0.001465576,0.7650897,0.0006544045,0.000373171,0.003508947,0.0009861364,0.0002584584,0.002183531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1103455,"threshold_uncertainty_score":0.5835695,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2796386963","doi":"10.1214/24-ejs2256","title":"Computationally efficient inference for latent position network models","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Vienna Science and Technology Fund; Insight SFI Research Centre for Data Analytics; Science Foundation Ireland","keywords":"Inference; Computer science; Markov chain Monte Carlo; Computational complexity theory; Algorithm; Markov chain; Likelihood function; Mathematical optimization; Statistical inference; Mathematics; Monte Carlo method; Theoretical computer science; Machine learning; Artificial intelligence; Estimation theory; Statistics","authors":[{"name":"Riccardo Rastelli","is_ca":false},{"name":"Florian Maire","is_ca":true},{"name":"Nial Friel","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04421908732242025,"gpt":0.3578300541177235,"spread":0.3136109667953032,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003635814,0.0008513478,0.001570664,0.001858332,0.001062608,0.001926697,0.002941805,0.001991468,0.004990209],"category_scores_gemma":[0.02927304,0.001080862,0.001124805,0.002000532,0.001817298,0.003732814,0.002371069,0.00324676,0.001060151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001870298,"about_ca_system_score_gemma":0.002120286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006499289,"about_ca_topic_score_gemma":0.01170411,"domain_scores_codex":[0.9978421,0.001256159,0.00007872949,0.0003443501,0.0003284509,0.0001501883],"domain_scores_gemma":[0.9789891,0.01839423,0.0007781133,0.001106297,0.0004355921,0.0002967586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000126527,0.00009735182,0.002104751,0.0001316242,0.00006941495,0.0001823878,0.0002029456,0.7958161,0.0007346655,0.1632993,0.001939571,0.03529534],"study_design_scores_gemma":[0.00001148169,0.0000038577,0.00008782847,0.000006710409,0.000004157807,0.00001537656,0.0000120144,0.9306852,0.0001196445,0.06878205,0.00026697,0.000004726169],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00804892,0.0001099648,0.9904129,0.0002482978,0.00001520796,0.00002748609,0.0001090889,0.000291925,0.0007362235],"genre_scores_gemma":[0.4046689,0.0004266848,0.5890748,0.000253287,0.000192731,0.000393932,0.001204092,0.0003778664,0.003407694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006499289,"threshold_uncertainty_score":0.01922822,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2000366201","doi":"10.1214/14-ejs920rej","title":"Rejoinder of “Dynamic treatment regimes: Technical challenges and applications”","year":2014,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; University of Waterloo; National Institute on Drug Abuse; University of Michigan; National Institutes of Health; National Science Foundation","keywords":"Mathematics; Econometrics; Applied mathematics","authors":[{"name":"Eric B. Laber","is_ca":false},{"name":"Daniel J. Lizotte","is_ca":true},{"name":"Min Qian","is_ca":false},{"name":"William E. Pelham","is_ca":false},{"name":"Susan A. Murphy","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2101329132993492,"gpt":0.4842567246269433,"spread":0.2741238113275941,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08870921,0.001376099,0.001823826,0.001307372,0.005720708,0.008059513,0.005410548,0.03210068,0.005403388],"category_scores_gemma":[0.29328,0.0008300499,0.00242231,0.001378444,0.01342342,0.01530138,0.007648096,0.0432017,0.002637036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004027762,"about_ca_system_score_gemma":0.004701803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001534425,"about_ca_topic_score_gemma":0.001012408,"domain_scores_codex":[0.9242531,0.05134948,0.005442069,0.006420851,0.01100405,0.001530391],"domain_scores_gemma":[0.7183964,0.2340313,0.006466492,0.007894529,0.02925099,0.003960345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000260255,0.00004493976,0.0004976765,0.0004020804,0.00008581604,0.0008128624,0.009580368,0.0006400051,0.000724036,0.319686,0.6384708,0.02879502],"study_design_scores_gemma":[0.00006402703,0.0000520079,0.0003150764,0.0005041272,0.00005040404,0.0005881104,0.005165598,0.002139505,0.0007571743,0.2353984,0.7548281,0.000137363],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.000684095,0.001656659,0.01078202,0.9630351,0.02201188,0.00002876266,0.00004361025,0.00005643412,0.001701401],"genre_scores_gemma":[0.04053217,0.002608728,0.02574147,0.8506704,0.07161598,0.0004944662,0.00006753296,0.0003601138,0.007909219],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.08870921,"threshold_uncertainty_score":0.4691448,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1964967113","doi":"10.1214/14-ejs919","title":"Model verification for Lévy-driven Ornstein-Uhlenbeck processes","year":2014,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ornstein–Uhlenbeck process; Mathematics; Statistic; Stochastic process; Process (computing); Lévy process; Applied mathematics; Test statistic; Statistical physics; Statistics; Statistical hypothesis testing; Computer science","authors":[{"name":"Ibrahim Abdelrazeq","is_ca":true},{"name":"B. Gail Ivanoff","is_ca":true},{"name":"Rafał Kulik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02551419858357542,"gpt":0.2346727136922442,"spread":0.2091585151086688,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00522209,0.0009221472,0.001736495,0.001104904,0.0008423389,0.002048395,0.002086016,0.002584131,0.00292048],"category_scores_gemma":[0.02436414,0.000532258,0.001684751,0.0005544728,0.001576954,0.002053927,0.002433924,0.002233183,0.0005079907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00181858,"about_ca_system_score_gemma":0.003101227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01020299,"about_ca_topic_score_gemma":0.004048225,"domain_scores_codex":[0.9980968,0.0005884658,0.0001092904,0.0002720126,0.0006874966,0.0002458937],"domain_scores_gemma":[0.9880564,0.008178575,0.001089621,0.0006401466,0.001727354,0.0003078674],"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":[0.0001374246,0.00008048607,0.004003763,0.0001230977,0.00006996293,0.0005363597,0.0002328442,0.7856309,0.003365574,0.1949774,0.0008095952,0.01003264],"study_design_scores_gemma":[0.00001269972,0.00001994605,0.000177814,0.000007578217,0.00000589661,0.00003398794,0.00001396528,0.9726335,0.0004404662,0.02646186,0.000181919,0.0000103819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09491191,0.0004583804,0.8983171,0.0005703772,0.00006709924,0.00007846116,0.0001765373,0.000503882,0.004916211],"genre_scores_gemma":[0.9715456,0.0002133359,0.02509965,0.0001003302,0.00003672332,0.00009984453,0.000210308,0.00006798462,0.002626183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01020299,"threshold_uncertainty_score":0.02761739,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056007064","doi":"10.1214/07-ejs126","title":"Optimal properties of some Bayesian inferences","year":2008,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Surprise; Bayesian probability; Equivalence (formal languages); Bayes' theorem; Prior probability; Bayes factor; Bayes' rule; Bayesian inference","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01437593574944648,"gpt":0.227984059872385,"spread":0.2136081241229386,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02290862,0.001370559,0.002728738,0.003041766,0.001734502,0.004817236,0.002647504,0.002993214,0.0099685],"category_scores_gemma":[0.12096,0.00162908,0.001682267,0.002064656,0.004146139,0.007901122,0.003583722,0.004219966,0.001091874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003408259,"about_ca_system_score_gemma":0.002718951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002021263,"about_ca_topic_score_gemma":0.001370764,"domain_scores_codex":[0.989336,0.006349067,0.0005343414,0.001645682,0.001557392,0.0005775587],"domain_scores_gemma":[0.9108536,0.07974984,0.002900352,0.003258864,0.002520463,0.0007169208],"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":[0.0002930262,0.00005451846,0.001302343,0.0003703396,0.0001405237,0.0001675298,0.0003930911,0.1234908,0.0007876228,0.8298184,0.003555675,0.03962618],"study_design_scores_gemma":[0.00004533857,0.00003676619,0.0003398439,0.00008394141,0.00003787641,0.0000650095,0.00004280771,0.2588745,0.0005410099,0.7388276,0.001083386,0.00002189805],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04018031,0.001182559,0.9323689,0.003170956,0.00006605755,0.0001500636,0.0008231587,0.000384926,0.0216731],"genre_scores_gemma":[0.6923971,0.0021297,0.2955464,0.001107044,0.0006141644,0.0006459539,0.001233063,0.0004505494,0.005875927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02290862,"threshold_uncertainty_score":0.1211538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2040542491","doi":"10.1214/14-ejs891","title":"Bayesian inference in partially identified models: Is the shape of the posterior distribution useful?","year":2014,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Posterior probability; Bayesian probability; Identification (biology); Posterior predictive distribution; Inference; Bayesian inference; Bayesian linear regression; Distribution (mathematics); Pattern recognition (psychology); Limit (mathematics); Artificial intelligence; Prior probability; Statistics; Computer science; Mathematical analysis","authors":[{"name":"Paul Gustafson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05165653981186841,"gpt":0.3289456219064409,"spread":0.2772890820945725,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03901441,0.001236292,0.003124601,0.001891167,0.001445911,0.005264754,0.003907701,0.004229902,0.002673482],"category_scores_gemma":[0.2618583,0.001536334,0.001811525,0.002806552,0.009468673,0.0181564,0.003667186,0.006632018,0.0005215419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002438707,"about_ca_system_score_gemma":0.001930058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003424346,"about_ca_topic_score_gemma":0.002607403,"domain_scores_codex":[0.9768037,0.01837342,0.0005490856,0.002128155,0.001526095,0.0006195906],"domain_scores_gemma":[0.6599328,0.3110777,0.01153286,0.01309058,0.003006245,0.001359776],"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":[0.0001802804,0.00006155756,0.003781212,0.0003567504,0.0002324922,0.0003252962,0.0007832191,0.06086889,0.0003668431,0.8998525,0.001618967,0.03157194],"study_design_scores_gemma":[0.00002090383,0.00002124237,0.000495426,0.00007903812,0.00003107665,0.0001080508,0.00008169059,0.08529741,0.0001658744,0.9129652,0.0007111836,0.00002295525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0331096,0.002713544,0.9512073,0.008918868,0.00008088473,0.00003781855,0.0002303763,0.0001392032,0.003562371],"genre_scores_gemma":[0.8242187,0.005463631,0.1645864,0.002124485,0.0008723679,0.0002284725,0.0003958241,0.000179768,0.001930309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03901441,"threshold_uncertainty_score":0.2063304,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2507260207","doi":"10.1214/16-ejs1169","title":"Designing penalty functions in high dimensional problems: The role of tuning parameters","year":2016,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"National Institute of General Medical Sciences; National Cancer Institute","keywords":"Penalty method; Curse of dimensionality; Covariate; Feature selection; Selection (genetic algorithm); Mathematics; Mathematical optimization; Computer science; Statistics; Machine learning","authors":[{"name":"Ting-Huei Chen","is_ca":true},{"name":"Wei Sun","is_ca":true},{"name":"Jason P. Fine","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008034378052642056,"gpt":0.2270823511662621,"spread":0.2190479731136201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02230769,0.003262403,0.002941726,0.001636129,0.001036137,0.00284003,0.002553098,0.004996533,0.001554859],"category_scores_gemma":[0.08756571,0.001493486,0.001111992,0.001802845,0.004372868,0.005060726,0.003506921,0.005644247,0.000619352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113067,"about_ca_system_score_gemma":0.002360831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001381443,"about_ca_topic_score_gemma":0.0008198012,"domain_scores_codex":[0.9900841,0.006985333,0.0005235061,0.0008942087,0.001091416,0.0004216012],"domain_scores_gemma":[0.928793,0.06276932,0.002585797,0.002234965,0.002830514,0.000786558],"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":[0.0002635091,0.0003090773,0.003882873,0.00102035,0.0002518646,0.000385345,0.0004820079,0.7615021,0.004956114,0.1239254,0.004134052,0.09888737],"study_design_scores_gemma":[0.00005384474,0.0001251733,0.0006019394,0.0001822067,0.0000273208,0.0001397436,0.00008451737,0.9434137,0.001084437,0.05195567,0.002271747,0.00005972906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007536564,0.001155212,0.9890943,0.0007792198,0.000068024,0.00008269188,0.00002464035,0.0001282557,0.00113105],"genre_scores_gemma":[0.2944644,0.003657842,0.6957589,0.00136632,0.0004925447,0.001125878,0.0002400129,0.0004755374,0.002418584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02230769,"threshold_uncertainty_score":0.1179758,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2998961846","doi":"10.1214/19-ejs1664","title":"Efficient estimation in expectile regression using envelope models","year":2020,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Estimator; Generalization; Statistics; Conditional probability distribution; Consistency (knowledge bases); Regression; Applied mathematics; Regression analysis; Asymptotic distribution; Econometrics; Mathematical optimization","authors":[{"name":"Tuo Chen","is_ca":false},{"name":"Zhihua Su","is_ca":false},{"name":"Yi Yang","is_ca":true},{"name":"Shanshan Ding","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1189849950312556,"gpt":0.3787226413180383,"spread":0.2597376462867828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01181891,0.001517165,0.002399395,0.001335526,0.0004172358,0.001610111,0.002491349,0.001834019,0.002781583],"category_scores_gemma":[0.04206573,0.0008897951,0.001825859,0.001576797,0.001367376,0.003937632,0.002892259,0.003077694,0.001314949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006332576,"about_ca_system_score_gemma":0.001050225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002070248,"about_ca_topic_score_gemma":0.001553156,"domain_scores_codex":[0.9944867,0.003670667,0.000200847,0.0007028604,0.0006322297,0.000306647],"domain_scores_gemma":[0.9802126,0.0151354,0.001352067,0.002033622,0.001066614,0.0001996948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003093326,0.0001376294,0.01010611,0.0003274706,0.0003277008,0.0004233668,0.0003114188,0.6479682,0.002820656,0.1868819,0.003174058,0.1472123],"study_design_scores_gemma":[0.0000121563,0.00004508853,0.001097739,0.00002925872,0.00002355845,0.00007954671,0.00002509169,0.9458532,0.0005972988,0.05115415,0.001056381,0.00002640967],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005568534,0.0001552844,0.9935325,0.0001100009,0.00001225097,0.00001691535,0.00007301973,0.0001797638,0.0003517265],"genre_scores_gemma":[0.5322323,0.001530931,0.4557318,0.0005815149,0.0002544312,0.0004284211,0.001616342,0.0004658868,0.007158375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01181891,"threshold_uncertainty_score":0.06250507,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2976853586","doi":"10.1214/19-ejs1595","title":"The tail empirical process for long memory stochastic volatility models with leverage","year":2019,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Estimator; Leverage (statistics); Mathematics; Econometrics; Stochastic volatility; Volatility (finance); Long memory; Limiting; Empirical research; Statistical physics; Statistics","authors":[{"name":"Clémonell Bilayi-Biakana","is_ca":true},{"name":"Gail Ivanoff","is_ca":true},{"name":"Rafał Kulik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0363387763138182,"gpt":0.2663582059718195,"spread":0.2300194296580013,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003335814,0.001242302,0.001593352,0.001600394,0.0007078046,0.002488887,0.001619491,0.002465446,0.004432803],"category_scores_gemma":[0.02230748,0.0004939907,0.001350739,0.001292485,0.002845463,0.00421908,0.002120476,0.003053025,0.0005272766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063967,"about_ca_system_score_gemma":0.0006769848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002732717,"about_ca_topic_score_gemma":0.001570265,"domain_scores_codex":[0.9992478,0.0002997901,0.00003735793,0.0001361526,0.0001335085,0.0001453844],"domain_scores_gemma":[0.9923389,0.004061945,0.001733871,0.0005586313,0.0006615194,0.0006451765],"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":[0.00009906455,0.00009257426,0.006311885,0.0001734391,0.00008302007,0.0007993719,0.0004053539,0.09162889,0.004041421,0.8871592,0.001609277,0.007596466],"study_design_scores_gemma":[0.00002853363,0.00005893047,0.001853044,0.0000740805,0.00005139894,0.0003265591,0.000122399,0.7014827,0.000709772,0.2942344,0.001000758,0.00005748224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2673037,0.002711187,0.7136368,0.002365053,0.0001468187,0.00009873934,0.0002520755,0.0004078224,0.01307786],"genre_scores_gemma":[0.9744601,0.001353596,0.01524991,0.0003385947,0.0002591203,0.0001055908,0.0001982703,0.00008309853,0.00795171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004432803,"threshold_uncertainty_score":0.01764172,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400131760","doi":"10.1214/24-ejs2253","title":"Post-selection inference for e-value based confidence intervals","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Confidence interval; Inference; Statistics; Selection (genetic algorithm); Value (mathematics); Confidence distribution; Artificial intelligence; Computer science","authors":[{"name":"Ziyu Xu","is_ca":false},{"name":"Ruodu Wang","is_ca":true},{"name":"Aaditya Ramdas","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2522498838553947,"gpt":0.5428647165319656,"spread":0.2906148326765708,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06045689,0.001715687,0.002331351,0.003389304,0.001127728,0.003971824,0.004890773,0.003268417,0.003859556],"category_scores_gemma":[0.3476971,0.001179669,0.001708344,0.003008073,0.005930889,0.005968892,0.005075202,0.008233076,0.0007000543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001891617,"about_ca_system_score_gemma":0.002030562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001459581,"about_ca_topic_score_gemma":0.0009062337,"domain_scores_codex":[0.9576159,0.02792033,0.00168038,0.006203548,0.005687006,0.000892785],"domain_scores_gemma":[0.5841172,0.3632276,0.0154479,0.02941284,0.006552223,0.001242248],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005234916,0.0002265514,0.01563202,0.0006295733,0.0005562321,0.0004377905,0.001001693,0.118314,0.00379649,0.6119792,0.002590561,0.2443124],"study_design_scores_gemma":[0.0001590381,0.0002625172,0.003091868,0.0002435476,0.0001196056,0.0002352194,0.00009410282,0.5892811,0.01080709,0.3918032,0.003806773,0.00009595993],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006536818,0.0002104055,0.9917985,0.0001949185,0.00003606273,0.0000668998,0.00007173366,0.0003126762,0.0007719302],"genre_scores_gemma":[0.3324209,0.0004115191,0.664108,0.000492058,0.000251109,0.0005327183,0.0004093612,0.0003175077,0.001056813],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9395431,"threshold_uncertainty_score":0.3197304,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1976117780","doi":"10.1214/12-ejs733","title":"Estimation of the mean for spatially dependent data belonging to a Riemannian manifold","year":2012,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Mathematics; Variogram; Estimator; Riemannian manifold; Covariance; Positive-definite matrix; Covariance matrix; Applied mathematics; Manifold (fluid mechanics); Statistics; Spatial analysis; Field (mathematics); Kriging; Pure mathematics","authors":[{"name":"Davide Pigoli","is_ca":false},{"name":"Piercesare Secchi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04659381972978054,"gpt":0.3270769032485266,"spread":0.2804830835187461,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004630731,0.0005873758,0.001016901,0.001654321,0.0003478379,0.0009423165,0.001119601,0.0009013085,0.0007066271],"category_scores_gemma":[0.0215023,0.000390519,0.0009216601,0.001121,0.001678103,0.001490254,0.001242145,0.001251952,0.0002858748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008000222,"about_ca_system_score_gemma":0.001128574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004162481,"about_ca_topic_score_gemma":0.003357025,"domain_scores_codex":[0.99864,0.0006644143,0.0000730034,0.0002969858,0.0002413483,0.00008436348],"domain_scores_gemma":[0.9911243,0.00566218,0.001200063,0.001084138,0.0007444079,0.0001849383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002389716,0.0001050683,0.04494882,0.0003988955,0.0004523247,0.0005989295,0.0008247039,0.4419388,0.02275017,0.2614733,0.002885552,0.2233844],"study_design_scores_gemma":[0.000008908041,0.0001035454,0.01570133,0.00004149229,0.00003811634,0.0002958187,0.00009664882,0.9083484,0.003411039,0.0699213,0.001970854,0.00006256677],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04630094,0.000205636,0.952777,0.0001531415,0.00001183813,0.00002007245,0.000101099,0.0001171345,0.0003131695],"genre_scores_gemma":[0.717196,0.000895651,0.2789616,0.0001364773,0.00009321672,0.0001313972,0.000947577,0.0001240329,0.001514131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004630731,"threshold_uncertainty_score":0.02448994,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393169903","doi":"10.1214/24-ejs2234","title":"Differentially private confidence intervals for proportions under stratified random sampling","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Statistics; Stratified sampling; Confidence interval; Sampling (signal processing); Econometrics","authors":[{"name":"Shurong Lin","is_ca":false},{"name":"Mark Bun","is_ca":false},{"name":"Eric D. Kolaczyk","is_ca":true},{"name":"Adam Smith","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04155910726246438,"gpt":0.3253740692689787,"spread":0.2838149620065143,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03135193,0.0009504366,0.001607101,0.002439816,0.0009284866,0.004522851,0.003722174,0.002352954,0.002453491],"category_scores_gemma":[0.2146252,0.0007886791,0.001610387,0.002779521,0.004196196,0.006147964,0.005861673,0.003682092,0.000794143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002690941,"about_ca_system_score_gemma":0.002635019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008633137,"about_ca_topic_score_gemma":0.0004923942,"domain_scores_codex":[0.9700578,0.01687724,0.001561264,0.003484006,0.006908303,0.001111311],"domain_scores_gemma":[0.8464805,0.1063395,0.01127839,0.02670536,0.007843775,0.001352284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006814475,0.00009818005,0.006525007,0.0002427343,0.0001760865,0.0002349326,0.001014746,0.1477326,0.003826858,0.7094187,0.001785263,0.1282636],"study_design_scores_gemma":[0.0001416544,0.0002203248,0.001851878,0.0001352383,0.00008124064,0.0004918848,0.0001629742,0.4520279,0.009389134,0.5308822,0.004524965,0.00009068374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008811872,0.0001864131,0.9894921,0.0002232165,0.000025203,0.00007540036,0.0001469815,0.0001828904,0.0008560497],"genre_scores_gemma":[0.4791347,0.0006454146,0.5168908,0.0003524507,0.0001565706,0.0006845864,0.0006842861,0.0001411739,0.001310069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03135193,"threshold_uncertainty_score":0.1658069,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392741924","doi":"10.1214/24-ejs2228","title":"Merging sequential e-values via martingales","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Econometrics; Statistics","authors":[{"name":"Vladimir Vovk","is_ca":false},{"name":"Ruodu Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009082415661675813,"gpt":0.2720718434848077,"spread":0.2629894278231319,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02386891,0.001270576,0.001855357,0.003046788,0.001370757,0.003463295,0.003335572,0.002378052,0.003595136],"category_scores_gemma":[0.0651875,0.001155538,0.002318416,0.003087866,0.004977352,0.0112258,0.005928054,0.003932985,0.0007382006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001860096,"about_ca_system_score_gemma":0.001609561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048308,"about_ca_topic_score_gemma":0.000668209,"domain_scores_codex":[0.9902629,0.004711256,0.0006464012,0.001808506,0.001755911,0.0008150819],"domain_scores_gemma":[0.9468873,0.04050838,0.004182544,0.004964072,0.00223836,0.001219368],"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":[0.0002871125,0.0001461573,0.005335097,0.0002601864,0.0001769113,0.0003419883,0.0008676353,0.109848,0.003467658,0.7785088,0.0008763136,0.0998841],"study_design_scores_gemma":[0.0000505776,0.0002258258,0.0008612953,0.0001065436,0.0000684251,0.0001784357,0.0001348707,0.3011792,0.007728289,0.6846924,0.004696935,0.00007715408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02188296,0.000310299,0.9762131,0.0002528469,0.00003456643,0.00007099838,0.00004589691,0.0001309469,0.001058493],"genre_scores_gemma":[0.4167366,0.000598319,0.5772626,0.0003687786,0.0001705852,0.0004073599,0.0002696025,0.0002140057,0.003972143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02386891,"threshold_uncertainty_score":0.1262323,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2052562214","doi":"10.1214/09-ejs533","title":"Asymptotic results for spatial causal ARMA models","year":2010,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Mathematics; Independent and identically distributed random variables; Central limit theorem; Martingale (probability theory); Applied mathematics; Zero (linguistics); Econometrics; Statistics; Random variable","authors":[{"name":"B. Gail Ivanoff","is_ca":true},{"name":"N. C. Weber","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01856317668232119,"gpt":0.2361154405911402,"spread":0.217552263908819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009024726,0.0009968161,0.001244545,0.003228905,0.0007799672,0.001925137,0.002072883,0.001464507,0.009395155],"category_scores_gemma":[0.04171429,0.0006696114,0.001863635,0.002158086,0.00259101,0.004222596,0.002644676,0.002955082,0.0008792748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001854013,"about_ca_system_score_gemma":0.001660831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003786968,"about_ca_topic_score_gemma":0.002946317,"domain_scores_codex":[0.9980739,0.0009563026,0.00008953879,0.0002396571,0.000474526,0.0001660311],"domain_scores_gemma":[0.9791993,0.0157114,0.00150339,0.001308123,0.001884354,0.0003934324],"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":[0.00002415212,0.00002815495,0.001142559,0.0001019658,0.00006455823,0.0001087165,0.0001149847,0.08242004,0.0004009897,0.9038293,0.001617834,0.01014675],"study_design_scores_gemma":[0.0000105375,0.00001445823,0.0004912236,0.0000501737,0.00002965824,0.00008639872,0.0000476085,0.390411,0.0001935305,0.6067067,0.001938619,0.00002010905],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02214185,0.002527143,0.96004,0.001432825,0.0001258041,0.00002732363,0.0002456305,0.0003491147,0.01311028],"genre_scores_gemma":[0.875926,0.005655963,0.09797448,0.0009640281,0.0008938955,0.0003541128,0.0009529694,0.0004475743,0.01683103],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009395155,"threshold_uncertainty_score":0.04772788,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4319442671","doi":"10.1214/23-ejs2112","title":"Functional spherical autocorrelation: A robust estimate of the autocorrelation of a functional time series","year":2023,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Autocorrelation; Mathematics; Series (stratigraphy); Estimator; Autocorrelation technique; Outlier; Statistics; Time series; Measure (data warehouse); Volatility (finance); Moving-average model; Unit root; Partial autocorrelation function; Applied mathematics; Econometrics; Algorithm; Computer science; Autoregressive integrated moving average; Data mining","authors":[{"name":"Chi-Kuang Yeh","is_ca":true},{"name":"Gregory Rice","is_ca":true},{"name":"Joel A. Dubin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02172708513542986,"gpt":0.2036483616219477,"spread":0.1819212764865178,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003147584,0.0008578672,0.0009015786,0.003070927,0.0004115047,0.001412486,0.001262373,0.0007307701,0.002021608],"category_scores_gemma":[0.01633202,0.0002738812,0.001002719,0.002789278,0.001156862,0.002301049,0.001173269,0.00120106,0.0005819236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005498857,"about_ca_system_score_gemma":0.001087993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004214607,"about_ca_topic_score_gemma":0.003208584,"domain_scores_codex":[0.9983908,0.0004434929,0.0001564491,0.0003406834,0.0005513681,0.0001171565],"domain_scores_gemma":[0.9913691,0.003531031,0.001803056,0.001287199,0.001771835,0.0002378077],"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":[0.0003370755,0.00017874,0.04411444,0.0006873055,0.000786841,0.0006444998,0.0004392287,0.293963,0.03222292,0.244161,0.009691504,0.3727734],"study_design_scores_gemma":[0.00002793189,0.0002012406,0.0188725,0.00008067112,0.0001063375,0.0004745268,0.0001433567,0.8960174,0.008136655,0.0664338,0.009325274,0.000180313],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02702848,0.00063161,0.9690657,0.0002461711,0.0001521574,0.00003708038,0.0003697838,0.0004374542,0.002031575],"genre_scores_gemma":[0.7380656,0.001230252,0.2554975,0.0003102684,0.0006171454,0.0001402713,0.001735194,0.000414426,0.001989305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004214607,"threshold_uncertainty_score":0.01664621,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3196880307","doi":"10.1214/22-ejs2026","title":"Estimation of cluster functionals for regularly varying time series: Runs estimators","year":2022,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Estimator; Mathematics; Series (stratigraphy); Multivariate statistics; Cluster (spacecraft); Limiting; Variance (accounting); Limit (mathematics); Central limit theorem; Statistics; Class (philosophy); Applied mathematics; Mathematical analysis; Computer science","authors":[{"name":"Youssouph Cissokho","is_ca":true},{"name":"Rafał Kulik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01440550226594523,"gpt":0.2229731777005734,"spread":0.2085676754346282,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003231815,0.0005096702,0.0007872277,0.001423431,0.0004004114,0.001035511,0.001618121,0.000886916,0.001482528],"category_scores_gemma":[0.01939194,0.0003207952,0.0005581771,0.001225795,0.001207228,0.00223007,0.001174582,0.001349886,0.0003030332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006058109,"about_ca_system_score_gemma":0.000643077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482658,"about_ca_topic_score_gemma":0.001432693,"domain_scores_codex":[0.9991779,0.0004071376,0.00002865186,0.0001729681,0.0001522007,0.00006103135],"domain_scores_gemma":[0.9926304,0.00459074,0.0009970091,0.0009942313,0.0005763921,0.0002112473],"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":[0.0002107374,0.0000985519,0.01605208,0.0001904751,0.0004509893,0.0001681983,0.0003301055,0.389392,0.009844308,0.5091761,0.00267447,0.0714119],"study_design_scores_gemma":[0.00001282885,0.00004442309,0.004281355,0.00003087232,0.00003688023,0.00006173685,0.00004270518,0.8639858,0.002253737,0.1280293,0.001175254,0.00004504797],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06372321,0.0003768048,0.9340237,0.0001009703,0.00002305613,0.00002385917,0.0001345846,0.0002520856,0.001341662],"genre_scores_gemma":[0.8035119,0.0006876776,0.1917187,0.00008631776,0.0001774608,0.0001383254,0.0006989618,0.0003244564,0.002656156],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003231815,"threshold_uncertainty_score":0.01709163,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4383269321","doi":"10.1214/23-ejs2133","title":"Uniform confidence bands for hazard functions from censored prevalent cohort survival data","year":2023,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"McGill University","funders":"Health Canada; Medical Research Council; Pfizer Canada; Macquarie University; Medical Research Council Canada; McGill University; Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Estimator; Mathematics; Hazard ratio; Statistics; Hazard; Consistency (knowledge bases); Nonparametric statistics; Confidence interval; Econometrics; Population; Asymptotic distribution; Mathematical optimization; Applied mathematics; Medicine","authors":[{"name":"Ali Shariati","is_ca":false},{"name":"Hassan Doosti","is_ca":false},{"name":"Vahid Fakoor‎","is_ca":false},{"name":"Masoud Asgharian","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1213334827919818,"gpt":0.3864293838223519,"spread":0.2650959010303701,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07068297,0.001225511,0.001926167,0.006864295,0.001083061,0.003697186,0.005238148,0.00278195,0.003606964],"category_scores_gemma":[0.3788167,0.001013132,0.002303502,0.003626105,0.005390726,0.005588197,0.005676299,0.005783335,0.0007150607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00172319,"about_ca_system_score_gemma":0.001963204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140887,"about_ca_topic_score_gemma":0.001189148,"domain_scores_codex":[0.9780164,0.01403087,0.00126306,0.002375423,0.003530976,0.0007832666],"domain_scores_gemma":[0.6933864,0.2694959,0.01215126,0.01772368,0.006247838,0.0009948349],"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":[0.0007254205,0.0002200927,0.01265377,0.001074011,0.0004110771,0.0005271407,0.001593318,0.1598528,0.002083475,0.6619496,0.002511222,0.1563982],"study_design_scores_gemma":[0.0001312769,0.0002708331,0.007266555,0.0007463264,0.0001449043,0.0005226404,0.0003447053,0.4901196,0.004136305,0.490411,0.005784272,0.0001215164],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009910904,0.0005232093,0.9882086,0.0001371769,0.00002782828,0.0001254975,0.000129284,0.0002184303,0.0007192335],"genre_scores_gemma":[0.4111046,0.001798034,0.580865,0.0004362183,0.0002066817,0.002005481,0.00163973,0.0003234997,0.001620832],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07068297,"threshold_uncertainty_score":0.3738118,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2938215358","doi":"10.1214/19-ejs1548","title":"Improved inference in generalized mean-reverting processes with multiple change-points","year":2019,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Mathematics; Inference; Mean reversion; Applied mathematics; Statistics; Econometrics; Calculus (dental); Artificial intelligence; Computer science","authors":[{"name":"Sévérien Nkurunziza","is_ca":true},{"name":"Kang Fu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05843957448055928,"gpt":0.3409529115552135,"spread":0.2825133370746542,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01375685,0.001183922,0.002330423,0.001141699,0.0004200443,0.001391853,0.002531239,0.001988409,0.001737818],"category_scores_gemma":[0.04635013,0.0007797193,0.001881499,0.001198888,0.002203734,0.003987255,0.001918673,0.002854849,0.0002993482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005990951,"about_ca_system_score_gemma":0.0008653483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001611844,"about_ca_topic_score_gemma":0.001149238,"domain_scores_codex":[0.9956241,0.001912197,0.000241182,0.00124658,0.0007202536,0.0002556931],"domain_scores_gemma":[0.972139,0.02216193,0.002213323,0.001991492,0.00120571,0.0002885372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004671414,0.0001281005,0.01013823,0.0006227021,0.0005398878,0.001257363,0.0005774738,0.6072378,0.006394329,0.2563849,0.0009986401,0.1152536],"study_design_scores_gemma":[0.00004090677,0.0001272689,0.001532515,0.00003206616,0.00008753949,0.0002055943,0.00003466491,0.922193,0.001759148,0.0732704,0.0006792672,0.00003759827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02683127,0.0003936257,0.9719489,0.0001710606,0.00003093417,0.0000203839,0.00004801356,0.0001096087,0.00044617],"genre_scores_gemma":[0.7377846,0.001254226,0.2556775,0.0003517716,0.0002989931,0.0001355387,0.0004248441,0.0001237086,0.003948771],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01375685,"threshold_uncertainty_score":0.07275409,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084721932","doi":"10.1214/08-ejs233","title":"Functional asymptotic confidence intervals for a common mean of independent random variables","year":2009,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Mathematics; Confidence interval; Statistics; Random variable","authors":[{"name":"Yuliya V. Martsynyuk","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06029869591390045,"gpt":0.3503027363291179,"spread":0.2900040404152174,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03896499,0.001308014,0.001878994,0.004085842,0.0006543598,0.003146648,0.004760343,0.003079833,0.002913984],"category_scores_gemma":[0.2312128,0.000537293,0.001746902,0.002687443,0.004651965,0.006135992,0.003659346,0.00414487,0.0006073441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001424376,"about_ca_system_score_gemma":0.001213474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007238542,"about_ca_topic_score_gemma":0.000294956,"domain_scores_codex":[0.9827313,0.009423828,0.0007868367,0.002331045,0.004066897,0.0006601373],"domain_scores_gemma":[0.7831525,0.1809485,0.01045408,0.01299773,0.01076995,0.001677193],"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":[0.0002187939,0.00006367018,0.0050365,0.0003007978,0.000220154,0.0003316567,0.0004122056,0.06566688,0.001304165,0.8558297,0.001487606,0.06912792],"study_design_scores_gemma":[0.00005151804,0.000166686,0.001573937,0.000186163,0.00005953619,0.0004858199,0.0001092761,0.2974331,0.002188823,0.6947681,0.002882834,0.00009414923],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01593093,0.0007685688,0.9802552,0.0004323667,0.00005342449,0.00003371163,0.0001127348,0.0001858362,0.002227084],"genre_scores_gemma":[0.6205292,0.001163329,0.3745506,0.0005537703,0.0004324588,0.0004905642,0.0006914744,0.0001738499,0.001414625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03896499,"threshold_uncertainty_score":0.206069,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2010125334","doi":"10.1214/12-ejs685","title":"Further asymptotic properties of the generalized information criterion","year":2012,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Applied mathematics; Statistics","authors":[{"name":"Changjiang Xu","is_ca":true},{"name":"A. Ian McLeod","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05102583955405139,"gpt":0.3076134556853414,"spread":0.25658761613129,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01878206,0.00212803,0.002454621,0.003242978,0.0009638944,0.003109545,0.002508857,0.002207227,0.009817258],"category_scores_gemma":[0.1591953,0.0006905965,0.002399652,0.003454494,0.003720332,0.007302076,0.003574613,0.006453595,0.001791353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001793569,"about_ca_system_score_gemma":0.002404715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308697,"about_ca_topic_score_gemma":0.001315223,"domain_scores_codex":[0.9909077,0.004997942,0.0005514518,0.001104632,0.002017624,0.0004208031],"domain_scores_gemma":[0.8873525,0.08942011,0.004944704,0.008659541,0.008813601,0.0008095049],"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":[0.00008297934,0.0001034412,0.002883811,0.0005421974,0.0001881614,0.0005636999,0.0003951737,0.06552365,0.002070266,0.8501118,0.006411703,0.07112313],"study_design_scores_gemma":[0.00002115672,0.00007532509,0.001430663,0.0001876389,0.0000825012,0.0005156505,0.0000923612,0.2190104,0.0009314571,0.7735091,0.004080997,0.00006265966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007953582,0.001691801,0.9773138,0.001540499,0.0002061186,0.00008157417,0.0002591576,0.0003388538,0.01061461],"genre_scores_gemma":[0.5146286,0.009862331,0.4512121,0.003152,0.002726504,0.001263205,0.002457028,0.0009860507,0.01371216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01878206,"threshold_uncertainty_score":0.09933025,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362648691","doi":"10.1214/23-ejs2124","title":"Improving estimation efficiency for two-phase, outcome-dependent sampling studies","year":2023,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariate; Statistics; Mathematics; Outcome (game theory); Sampling (signal processing); Selection (genetic algorithm); Sample size determination; Econometrics; Conditional probability distribution; Data mining; Computer science; Machine learning","authors":[{"name":"Menglu Che","is_ca":false},{"name":"Peisong Han","is_ca":false},{"name":"Jerald F. Lawless","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1233054509736014,"gpt":0.4787470123097977,"spread":0.3554415613361962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05250739,0.001136742,0.002014211,0.001860173,0.0005380002,0.001479429,0.002198557,0.001286247,0.003334131],"category_scores_gemma":[0.1650196,0.0008237927,0.001440928,0.002445269,0.001046781,0.001929407,0.002806733,0.002115387,0.0007026658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008181253,"about_ca_system_score_gemma":0.002467579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002551541,"about_ca_topic_score_gemma":0.003092726,"domain_scores_codex":[0.9698135,0.02667769,0.0008797012,0.001181276,0.001237735,0.0002102063],"domain_scores_gemma":[0.8705947,0.1169407,0.003189595,0.005654601,0.003132219,0.000488278],"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":[0.001384141,0.0004082972,0.03140582,0.001766018,0.001292453,0.0005990943,0.0009069724,0.1864099,0.005302337,0.1542416,0.007395518,0.6088879],"study_design_scores_gemma":[0.0003764596,0.0003191186,0.003707276,0.0001796075,0.0002237975,0.0002678921,0.0001330977,0.890532,0.001780541,0.09485344,0.007572743,0.00005407657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004313403,0.0003570422,0.994387,0.000231832,0.00002901065,0.0001548059,0.00006155264,0.0001449924,0.0003203758],"genre_scores_gemma":[0.09267519,0.0005039363,0.9046333,0.0002846081,0.00008793512,0.0006708893,0.0003351124,0.0001064956,0.0007024459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05250739,"threshold_uncertainty_score":0.2776889,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2595290096","doi":"10.1214/19-ejs1645","title":"Estimation of a bivariate conditional copula when a variable is subject to random right censoring","year":2019,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"","keywords":"Copula (linguistics); Mathematics; Estimator; Covariate; Bivariate analysis; Statistics; Censoring (clinical trials); Econometrics; Nonparametric statistics; Random variable; Conditional probability distribution","authors":[{"name":"Taoufik Bouezmarni","is_ca":true},{"name":"Félix Camirand Lemyre","is_ca":true},{"name":"Anouar El Ghouch","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02419213954672644,"gpt":0.3220268523370364,"spread":0.2978347127903099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007109237,0.0005745378,0.001589923,0.001009391,0.0003628996,0.00107239,0.001444757,0.0009630613,0.001140233],"category_scores_gemma":[0.03484295,0.0006161102,0.0008576027,0.001313362,0.001161763,0.001505404,0.001457484,0.001794786,0.0002532068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006366789,"about_ca_system_score_gemma":0.001271953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005139728,"about_ca_topic_score_gemma":0.003028342,"domain_scores_codex":[0.9978302,0.001413086,0.00008259484,0.0002752418,0.0002487789,0.0001501769],"domain_scores_gemma":[0.9827741,0.01364969,0.001346224,0.001353279,0.0006582065,0.0002184542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001761661,0.0001139173,0.02013835,0.0002343255,0.0003985075,0.0007320756,0.0002227946,0.7556072,0.002886857,0.1382058,0.002168063,0.07911599],"study_design_scores_gemma":[0.000007890832,0.00002143004,0.002190257,0.00001616653,0.00002528469,0.00007669943,0.00001895819,0.9807512,0.0004491584,0.01602235,0.0004064978,0.00001419053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03226446,0.0002648907,0.9666332,0.000164085,0.00001431027,0.00002609741,0.00007850678,0.0001190925,0.0004354275],"genre_scores_gemma":[0.7746375,0.0009978669,0.2218982,0.0001380077,0.0001026247,0.0001524148,0.0005829591,0.0001306695,0.001359845],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007109237,"threshold_uncertainty_score":0.03759772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1995451366","doi":"10.1214/08-ejs340","title":"Multidimensional hazard estimation under generalized censoring","year":2009,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Mathematics; Censoring (clinical trials); Estimator; Applied mathematics; Statistics; Central limit theorem; Mathematical optimization","authors":[{"name":"Alberto Carabarin Aguirre","is_ca":true},{"name":"B. Gail Ivanoff","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05984994758419908,"gpt":0.4024888969287458,"spread":0.3426389493445467,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01018487,0.0007113202,0.001580209,0.001146161,0.0003917283,0.00140626,0.002474868,0.001554033,0.001101202],"category_scores_gemma":[0.03348018,0.0004404288,0.001197848,0.001947285,0.00133538,0.001782422,0.00299902,0.001521242,0.000240042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005700247,"about_ca_system_score_gemma":0.0006647434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001126187,"about_ca_topic_score_gemma":0.0005282084,"domain_scores_codex":[0.9950646,0.003147233,0.00023464,0.0007044223,0.0006371707,0.0002120244],"domain_scores_gemma":[0.980872,0.01303234,0.002188538,0.002928891,0.0007190886,0.000259013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004728782,0.0001081303,0.01950706,0.0006114619,0.0005388288,0.001580207,0.000654489,0.5163245,0.003987229,0.2698916,0.00140463,0.1849191],"study_design_scores_gemma":[0.00004902551,0.0001022376,0.004000239,0.00004261738,0.00006094042,0.000403588,0.00007702005,0.8530989,0.001429582,0.1395195,0.00115337,0.00006295504],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02373934,0.0004687389,0.9751372,0.0001840521,0.00002607404,0.00001653788,0.00006902489,0.00009828451,0.0002608119],"genre_scores_gemma":[0.7573414,0.001437782,0.2384872,0.000234327,0.000247553,0.0001694344,0.0004311167,0.00005595644,0.001595205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01018487,"threshold_uncertainty_score":0.05386341,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387271850","doi":"10.1214/23-ejs2148","title":"Nonregular designs from Paley’s Hadamard matrices: Generalized resolution, projectivity and hidden projection property","year":2023,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"graph theory and CDMA systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Hadamard transform; Mathematics; Projection (relational algebra); Resolution (logic); Property (philosophy); Hadamard matrix; Algorithm; Computer science; Mathematical analysis; Artificial intelligence","authors":[{"name":"Guanzhou Chen","is_ca":false},{"name":"Chenlu Shi","is_ca":false},{"name":"Boxin Tang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01688612921967763,"gpt":0.2214820755366025,"spread":0.2045959463169248,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007029916,0.001065542,0.0008575052,0.001042078,0.0005141931,0.0009529573,0.001135479,0.0007088806,0.00411221],"category_scores_gemma":[0.02434385,0.000508195,0.001069451,0.0009864296,0.001664911,0.001746874,0.001474588,0.001439266,0.000673547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004803773,"about_ca_system_score_gemma":0.0009860491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001979585,"about_ca_topic_score_gemma":0.0002593191,"domain_scores_codex":[0.9936798,0.00383582,0.0003622605,0.0008755892,0.0009565419,0.0002900164],"domain_scores_gemma":[0.973078,0.01418484,0.003316995,0.006841869,0.00182556,0.000752667],"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":[0.001394637,0.0003155598,0.006497537,0.0006977118,0.0002719082,0.0003542712,0.0004916582,0.08733772,0.03394629,0.5663566,0.002595192,0.2997409],"study_design_scores_gemma":[0.0007741628,0.004976067,0.005624969,0.0002273468,0.0002101969,0.001377973,0.0002367296,0.33068,0.03549118,0.6046891,0.01543642,0.0002757915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08062366,0.0004065539,0.9140816,0.0001519839,0.00005579941,0.0002036436,0.0001480222,0.0002549114,0.004073888],"genre_scores_gemma":[0.4844911,0.0003381273,0.511987,0.0001948102,0.00009346627,0.0005323874,0.000236181,0.0001014261,0.002025432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007029916,"threshold_uncertainty_score":0.03717822,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400131791","doi":"10.1214/24-ejs2258","title":"Order statistics approaches to unobserved heterogeneity in auctions","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Mathematics; Statistics; Econometrics; Order (exchange); Common value auction; Order statistic; Economics","authors":[{"name":"Yao Luo","is_ca":true},{"name":"Peijun Sang","is_ca":true},{"name":"Ruli Xiao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2468184260250227,"gpt":0.3915112714668051,"spread":0.1446928454417825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01765195,0.001020604,0.002563682,0.003735557,0.001242653,0.00384634,0.003463545,0.002112554,0.004601054],"category_scores_gemma":[0.06091265,0.0009407587,0.003028795,0.003864415,0.003396495,0.004793558,0.002410161,0.004030337,0.0005214504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002469969,"about_ca_system_score_gemma":0.00224618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007064347,"about_ca_topic_score_gemma":0.006535614,"domain_scores_codex":[0.9915026,0.004880853,0.000551753,0.001386899,0.0009475891,0.0007303038],"domain_scores_gemma":[0.8950345,0.08044757,0.0114976,0.009893451,0.002146119,0.0009807261],"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":[0.0001524388,0.0001807214,0.03129192,0.0001603644,0.000425015,0.0004734501,0.0006429901,0.216897,0.0006571035,0.7165522,0.001872455,0.03069428],"study_design_scores_gemma":[0.00004958662,0.0000806207,0.005498619,0.00002870121,0.00005804432,0.00009701239,0.0001215919,0.5391167,0.0002784863,0.4536031,0.001015283,0.00005224998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1257014,0.0004620925,0.8693537,0.0009231641,0.0000419867,0.0001174735,0.0008722743,0.0002298312,0.002297955],"genre_scores_gemma":[0.9404665,0.0006134097,0.05368529,0.0001945301,0.0002315188,0.0002698522,0.001098194,0.00006098582,0.003379791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01765195,"threshold_uncertainty_score":0.09335351,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1967283657","doi":"10.1214/14-ejs937c","title":"Analysis of juggling data: Landmark and continuous registration of juggling trajectories","year":2014,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; McGill University","funders":"Ohio State University","keywords":"Landmark; Mathematics; Variation (astronomy); Acceleration; Ball (mathematics); Statistics; Artificial intelligence; Geometry; Computer science","authors":[{"name":"J. O. Ramsay","is_ca":true},{"name":"Paul L. Gribble","is_ca":true},{"name":"Sebastian Kurtek","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02264122270952678,"gpt":0.2671644501521926,"spread":0.2445232274426659,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001289982,0.0004095964,0.0007304042,0.001477217,0.0002823015,0.0010938,0.0004166376,0.0004338742,0.001519927],"category_scores_gemma":[0.008817883,0.0002447568,0.0004216149,0.002083475,0.0007661504,0.0008824167,0.0007786074,0.0006567264,0.0006519224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00026644,"about_ca_system_score_gemma":0.000723935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001403462,"about_ca_topic_score_gemma":0.002248446,"domain_scores_codex":[0.9989824,0.0002218547,0.00006928734,0.0003049652,0.0003075682,0.0001138829],"domain_scores_gemma":[0.9979572,0.0007482266,0.0002554056,0.0006391992,0.0003040694,0.00009585587],"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":[0.001888525,0.000176888,0.01983307,0.0003951396,0.0001677494,0.0001813143,0.0008353554,0.03078154,0.2223663,0.005251994,0.001788184,0.716334],"study_design_scores_gemma":[0.0001611102,0.001871007,0.4170363,0.0001235321,0.0002319059,0.001476357,0.001046464,0.4134385,0.1348063,0.01501326,0.0143926,0.0004026863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4424505,0.0006643335,0.5500215,0.00009742545,0.00009407868,0.0002246133,0.001046667,0.002008198,0.003392644],"genre_scores_gemma":[0.8687187,0.0002425374,0.1282264,0.00002222686,0.00004077757,0.000207316,0.001061939,0.0004643999,0.001015675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001519927,"threshold_uncertainty_score":0.006822169,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057605492","doi":"10.1214/07-ejs075","title":"New multivariate central limit theorems in linear structural and functional error-in-variables models","year":2007,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Mathematics; Estimator; Central limit theorem; Applied mathematics; Mathematical proof; Covariance; Studentized range; Context (archaeology); Errors-in-variables models; Limit (mathematics); Multivariate statistics; Statistics; Mathematical analysis; Standard error","authors":[{"name":"Yuliya V. Martsynyuk","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05743485376593672,"gpt":0.3381636304415188,"spread":0.280728776675582,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0320662,0.002081014,0.002422868,0.004016854,0.001208153,0.004575716,0.005031452,0.003333418,0.007232422],"category_scores_gemma":[0.09699058,0.001291016,0.003642654,0.003416912,0.005997449,0.009917152,0.00557605,0.00762089,0.001110234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003300869,"about_ca_system_score_gemma":0.0030116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878295,"about_ca_topic_score_gemma":0.001871483,"domain_scores_codex":[0.9880755,0.007299078,0.0005167466,0.001461066,0.002057793,0.0005898135],"domain_scores_gemma":[0.937604,0.0494534,0.005018766,0.003045156,0.004053026,0.0008256613],"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":[0.0000114904,0.0000192298,0.0004996177,0.0001470393,0.00006442636,0.00008977693,0.0001924709,0.01300788,0.0001207303,0.9753639,0.001207652,0.009275941],"study_design_scores_gemma":[0.00001415475,0.0000352737,0.0003746711,0.0001006767,0.00003283186,0.0001001433,0.00005099128,0.1254925,0.0001758467,0.8703825,0.003210881,0.00002952207],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005321478,0.001666622,0.9868104,0.00117186,0.0001851404,0.00003978983,0.0001867351,0.0001576584,0.004460339],"genre_scores_gemma":[0.4690312,0.00959602,0.4928201,0.003115834,0.002777837,0.00179469,0.001170714,0.001002011,0.01869152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0320662,"threshold_uncertainty_score":0.1695843,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3145349353","doi":"10.1214/21-ejs1831","title":"Graphical-model based high dimensional generalized linear models","year":2021,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Public Health Ontario; York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Crohn's and Colitis Canada; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Mathematics; Estimator; Model selection; Linear model; Generalized linear model; Graphical model; Lasso (programming language); Consistency (knowledge bases); Curse of dimensionality; Node (physics); Clustering high-dimensional data; Dimensionality reduction; Graph; High dimensional; Algorithm; Mathematical optimization; Applied mathematics; Computer science; Machine learning; Statistics; Artificial intelligence; Cluster analysis","authors":[{"name":"Yaguang Li","is_ca":true},{"name":"Wei Xu","is_ca":true},{"name":"Xin Gao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05851530656828702,"gpt":0.3404529522454549,"spread":0.2819376456771679,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004092115,0.001285919,0.002052426,0.001457612,0.0004648188,0.001833877,0.003181058,0.001693477,0.003022193],"category_scores_gemma":[0.01764088,0.0008251864,0.001723231,0.0024731,0.001825169,0.002186621,0.002211042,0.002854275,0.0009357571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008886132,"about_ca_system_score_gemma":0.001400736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004413071,"about_ca_topic_score_gemma":0.004260613,"domain_scores_codex":[0.9960551,0.002535645,0.00009959647,0.0006806865,0.0004473132,0.0001816717],"domain_scores_gemma":[0.9913803,0.006013615,0.001006504,0.00086454,0.0005656031,0.0001693277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000728198,0.00005321989,0.002612252,0.0002018623,0.0002503306,0.0001990639,0.0001078167,0.8168369,0.0007421615,0.1452435,0.002082571,0.03159735],"study_design_scores_gemma":[0.0000120426,0.00002974181,0.0002753023,0.00001366818,0.00002553139,0.00003040661,0.00001174525,0.9232455,0.0001159881,0.07546082,0.0007665943,0.00001260495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005584667,0.00025764,0.9929462,0.0003569828,0.00004124246,0.00002263817,0.0001666593,0.0001478281,0.0004760619],"genre_scores_gemma":[0.5253444,0.001624251,0.4645519,0.0007802108,0.0004084026,0.0005284273,0.001547003,0.0001748953,0.005040507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004413071,"threshold_uncertainty_score":0.02164143,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4220991108","doi":"10.1214/22-ejs1998","title":"Adaptive threshold-based classification of sparse high-dimensional data","year":2022,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Classifier (UML); Pattern recognition (psychology); Sample size determination; Binary classification; Feature (linguistics); Binary number; Diagonal; Covariance matrix; Linear classifier; Algorithm; Covariance; Artificial intelligence; Statistics; Computer science","authors":[{"name":"Tatjana Pavlenko","is_ca":false},{"name":"Natalia A. Stepanova","is_ca":true},{"name":"L.F. Thompson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07747682503576564,"gpt":0.3228351643396283,"spread":0.2453583393038627,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002799846,0.0004606647,0.001487709,0.0009344087,0.0003396551,0.001187965,0.001593616,0.00144026,0.0008680943],"category_scores_gemma":[0.01391477,0.0003398039,0.0005569882,0.001199178,0.001181788,0.001937769,0.001205981,0.001313477,0.0004914796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006856456,"about_ca_system_score_gemma":0.0007417267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001274868,"about_ca_topic_score_gemma":0.001017706,"domain_scores_codex":[0.9988652,0.0003533677,0.00008197389,0.0002194784,0.0003754157,0.0001045107],"domain_scores_gemma":[0.9937732,0.00425458,0.0006161994,0.0005932596,0.0006214032,0.000141539],"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":[0.000377353,0.0001704569,0.003504939,0.0002435732,0.0001012306,0.0001780349,0.0002080539,0.7235386,0.02666644,0.02985958,0.001572949,0.2135788],"study_design_scores_gemma":[0.000004603753,0.00002439046,0.0002456759,0.000002982976,0.000003624775,0.00001949769,0.000006492382,0.9937288,0.00140876,0.004415396,0.0001350007,0.000004843201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02421539,0.0001142623,0.9751604,0.0001223226,0.00001586313,0.00002256103,0.00003236585,0.0001244642,0.0001923583],"genre_scores_gemma":[0.591283,0.0003943215,0.4058369,0.0001792113,0.0001405774,0.0001893437,0.00034107,0.00007497361,0.001560631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002799846,"threshold_uncertainty_score":0.01480716,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4394954599","doi":"10.1214/24-ejs2241","title":"Exponential family trend filtering on lattices","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Exponential family; Exponential function; Applied mathematics; Statistics; Econometrics; Mathematical analysis","authors":[{"name":"Veeranjaneyulu Sadhanala","is_ca":false},{"name":"Robert Bassett","is_ca":false},{"name":"James Sharpnack","is_ca":false},{"name":"Daniel J. McDonald","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07627339575203877,"gpt":0.3707843799962394,"spread":0.2945109842442006,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00289934,0.0005038043,0.0009833473,0.001542612,0.0008656683,0.001802083,0.001286735,0.0009461084,0.005127571],"category_scores_gemma":[0.0182179,0.0005476168,0.001029022,0.001779346,0.001578908,0.002737939,0.001785903,0.001968373,0.001522833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126187,"about_ca_system_score_gemma":0.0009844655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366112,"about_ca_topic_score_gemma":0.002716015,"domain_scores_codex":[0.9982435,0.0006010826,0.00009649743,0.0003672648,0.0005032661,0.0001884036],"domain_scores_gemma":[0.9896116,0.006568825,0.000775162,0.001307073,0.001359642,0.0003777092],"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":[0.0001708771,0.00005040546,0.001978547,0.00008690283,0.00004200992,0.0001816888,0.0001706348,0.2284524,0.003151685,0.6856144,0.004229427,0.07587105],"study_design_scores_gemma":[0.00001899987,0.00002132659,0.0002562939,0.00001184577,0.000005128417,0.00004564179,0.00002432509,0.6774838,0.0005645821,0.3191601,0.002392897,0.00001496372],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01730993,0.0001445492,0.979684,0.0002011454,0.00003850389,0.00001499183,0.0001459324,0.0003165605,0.002144323],"genre_scores_gemma":[0.5161575,0.001003201,0.4621292,0.0003609211,0.0003290427,0.0002321082,0.001196098,0.0004906557,0.01810125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005127571,"threshold_uncertainty_score":0.01715344,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408201762","doi":"10.1214/25-ejs2359","title":"Resistant convex clustering: How does the fusion penalty enhance resistance?","year":2025,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Penalty method; Cluster analysis; Regular polygon; Mathematical optimization; Statistics","authors":[{"name":"Qiang Sun","is_ca":true},{"name":"Archer Gong Zhang","is_ca":true},{"name":"Chenyu Liu","is_ca":false},{"name":"Kean Ming Tan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.006119107416195306,"gpt":0.262812118280545,"spread":0.2566930108643496,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01720631,0.002743268,0.003262221,0.001511887,0.001457257,0.003371055,0.004104521,0.004923036,0.003587705],"category_scores_gemma":[0.08094021,0.001349801,0.002071093,0.001944946,0.003540456,0.007241746,0.005861128,0.00534817,0.002963695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421666,"about_ca_system_score_gemma":0.00178742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002129558,"about_ca_topic_score_gemma":0.002181461,"domain_scores_codex":[0.9886558,0.00667574,0.0004826785,0.001614818,0.002050392,0.0005207142],"domain_scores_gemma":[0.9630815,0.02185678,0.00294945,0.006736138,0.004337869,0.001038167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001252411,0.000301168,0.006947371,0.0008773237,0.0008799078,0.0004724941,0.001264343,0.5111627,0.01686939,0.1568554,0.01784155,0.2852759],"study_design_scores_gemma":[0.0001125555,0.0002802104,0.001197209,0.000152293,0.0001169037,0.0003493133,0.0001867654,0.9025481,0.006905321,0.07961889,0.008392435,0.0001399484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01545213,0.001055676,0.9781556,0.002063005,0.0001616267,0.00009408317,0.00009891628,0.0008623218,0.00205673],"genre_scores_gemma":[0.2980143,0.001470114,0.6902404,0.001887038,0.0004961679,0.0003289316,0.0006881276,0.001728041,0.005146968],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01720631,"threshold_uncertainty_score":0.09099674,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312834984","doi":"10.1214/22-ejs2069","title":"Semiparametric empirical likelihood inference with estimating equations under density ratio models","year":2022,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Empirical likelihood; Estimator; Quantile; Estimating equations; Statistics; Asymptotic distribution; Likelihood function; Inference; Applied mathematics; Likelihood-ratio test; Fisher information; Statistical inference; Confidence interval; Delta method; Generalized estimating equation; Ratio estimator; Estimation theory; Efficient estimator; Computer science","authors":[{"name":"Meng Yuan","is_ca":true},{"name":"Pengfei Li","is_ca":true},{"name":"Changbao Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07183809390455385,"gpt":0.3788179462929314,"spread":0.3069798523883775,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02640108,0.001463787,0.002501586,0.002326437,0.000444287,0.00292486,0.003510676,0.002490412,0.002845954],"category_scores_gemma":[0.1417329,0.001142382,0.001980881,0.002823398,0.002784316,0.005787647,0.004277181,0.004116326,0.000821398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210126,"about_ca_system_score_gemma":0.001350813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002277446,"about_ca_topic_score_gemma":0.001167502,"domain_scores_codex":[0.9788907,0.01685844,0.000657128,0.001460981,0.001779258,0.0003535169],"domain_scores_gemma":[0.8882281,0.09919209,0.004762237,0.005062867,0.002416661,0.0003381071],"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":[0.0001219512,0.00009643484,0.003849311,0.0003627062,0.0002867326,0.0003579948,0.0003695329,0.2010504,0.0009389303,0.7279146,0.001129147,0.06352226],"study_design_scores_gemma":[0.00004930154,0.00005356779,0.0005472085,0.0000492029,0.0000485659,0.0001434213,0.00003934217,0.6763556,0.000493351,0.3209349,0.001250498,0.00003506912],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003579555,0.000195535,0.9955097,0.0001377206,0.000009765493,0.00003069353,0.00005539498,0.00007239683,0.0004091862],"genre_scores_gemma":[0.2806358,0.001250767,0.7132748,0.0003588714,0.0002236478,0.0007554517,0.0005935598,0.0001532292,0.002753836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02640108,"threshold_uncertainty_score":0.1396239,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393227756","doi":"10.1214/24-ejs2235","title":"Geometric ergodicity of Gibbs samplers for Bayesian error-in-variable regression","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Ergodicity; Gibbs sampling; Statistics; Regression; Bayesian probability; Econometrics; Statistical physics; Applied mathematics","authors":[{"name":"Austin L. Brown","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06483931406611916,"gpt":0.3805997205616266,"spread":0.3157604064955075,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0160768,0.001459964,0.0021561,0.002459961,0.001342578,0.002165392,0.003785465,0.001898643,0.003593647],"category_scores_gemma":[0.05890912,0.001130115,0.002243371,0.001984316,0.005075195,0.003392695,0.00390162,0.00384398,0.0009437428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002276868,"about_ca_system_score_gemma":0.002530247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004925688,"about_ca_topic_score_gemma":0.005202794,"domain_scores_codex":[0.9948769,0.00333241,0.0001888274,0.0005689746,0.0006697095,0.0003632021],"domain_scores_gemma":[0.9554556,0.03687922,0.001697784,0.003124825,0.002107069,0.0007355269],"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":[0.00007586451,0.00002972405,0.001815021,0.00006851997,0.00009556702,0.0001144492,0.000143612,0.2524678,0.0005061714,0.7251371,0.0009688226,0.01857742],"study_design_scores_gemma":[0.00001609939,0.0000188297,0.0002343429,0.00002276366,0.00001964992,0.00004138817,0.00001446353,0.7500136,0.0003846549,0.2483479,0.000863094,0.00002321464],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006197339,0.0002287517,0.9923515,0.0001552555,0.00003222006,0.00002899649,0.00005476966,0.0001596314,0.0007914624],"genre_scores_gemma":[0.5206656,0.001620309,0.4652321,0.000507844,0.0004617948,0.0009197171,0.00129118,0.0007909995,0.008510451],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0160768,"threshold_uncertainty_score":0.08502328,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402412381","doi":"10.1214/24-ejs2284","title":"Selecting strong orthogonal arrays by linear allowable level permutations","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematics; Orthogonal array; Combinatorics; Statistics; Applied mathematics; Taguchi methods","authors":[{"name":"Guanzhou Chen","is_ca":false},{"name":"Boxin Tang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1282196979000005,"gpt":0.448128365770885,"spread":0.3199086678708845,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006836554,0.0009680617,0.001123462,0.001350768,0.0007127115,0.001169652,0.0008624998,0.0008917983,0.002925043],"category_scores_gemma":[0.02083996,0.0005717858,0.00105497,0.001492093,0.001938365,0.001621681,0.001679712,0.001201605,0.0008910206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004243574,"about_ca_system_score_gemma":0.001306668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002005813,"about_ca_topic_score_gemma":0.0003622399,"domain_scores_codex":[0.9895796,0.007333169,0.0004984251,0.0009293012,0.00125782,0.0004017336],"domain_scores_gemma":[0.9828758,0.01259329,0.001245711,0.00195276,0.001045174,0.0002872968],"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":[0.001114822,0.0004330941,0.004769897,0.0006446111,0.0001617115,0.0002487411,0.0004309751,0.1148598,0.03009521,0.4257726,0.003345878,0.4181226],"study_design_scores_gemma":[0.0003166924,0.001948166,0.001733791,0.0001116428,0.00009254826,0.0003022455,0.0001739182,0.4662741,0.01910315,0.4992915,0.01052838,0.0001239339],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03008796,0.0001277484,0.9670541,0.000108339,0.00003162475,0.0001881864,0.0001131839,0.000169966,0.002118893],"genre_scores_gemma":[0.2429533,0.0003074831,0.7530946,0.0002114702,0.00007374318,0.001778075,0.0003674294,0.00008371504,0.001130124],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006836554,"threshold_uncertainty_score":0.03615558,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406245583","doi":"10.1214/24-ejs2338","title":"Pseudo-empirical likelihood methods for causal inference","year":2025,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Empirical likelihood; Causal inference; Inference; Econometrics; Statistics; Artificial intelligence; Estimator; Computer science","authors":[{"name":"Jingyue Huang","is_ca":false},{"name":"Changbao Wu","is_ca":true},{"name":"Leilei Zeng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09943190733000731,"gpt":0.522855835819901,"spread":0.4234239284898937,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05034734,0.002218534,0.003388501,0.005061802,0.001131578,0.003740932,0.006335568,0.003562494,0.01394164],"category_scores_gemma":[0.2108259,0.001373711,0.003677588,0.006134617,0.00464246,0.006440348,0.004393968,0.006621293,0.002906215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179225,"about_ca_system_score_gemma":0.003349604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002086345,"about_ca_topic_score_gemma":0.001651196,"domain_scores_codex":[0.9527141,0.0400923,0.001288184,0.002132512,0.00344326,0.0003295715],"domain_scores_gemma":[0.8041922,0.1740413,0.005415277,0.01203446,0.00382889,0.0004878618],"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":[0.00008350832,0.00008331874,0.00144255,0.0007145826,0.0004391404,0.0002201316,0.0002973457,0.03556317,0.0002626261,0.8456662,0.002848716,0.1123788],"study_design_scores_gemma":[0.00007265337,0.00005861695,0.0004050618,0.0001785647,0.00007800875,0.0001944494,0.00006597045,0.1809124,0.0003527985,0.8103997,0.007237965,0.00004377952],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003282501,0.0003149858,0.998395,0.0001842177,0.00004207748,0.00008353588,0.00008282637,0.0001023953,0.000466629],"genre_scores_gemma":[0.06126457,0.001876514,0.9304169,0.000520583,0.0005313018,0.001991408,0.0006235657,0.0002967485,0.002478384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05034734,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"grok","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W3188800301","doi":"10.1214/21-ejs1875","title":"Noncausal counting processes: A queuing perspective","year":2021,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Université Paris 13; Agence Nationale de la Recherche","keywords":"Mathematics; Counting process; Markov process; Simple (philosophy); Queueing theory; Stochastic process; Process (computing); Affine transformation; Interpretation (philosophy); Applied mathematics; Statistics; Computer science; Pure mathematics","authors":[{"name":"Christian Gouriéroux","is_ca":true},{"name":"Yang Lu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00782399592962981,"gpt":0.2496440788880564,"spread":0.2418200829584266,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002909201,0.001042467,0.0007126654,0.001383894,0.00102673,0.002822748,0.002354357,0.001543753,0.005962691],"category_scores_gemma":[0.008439488,0.0005819283,0.0009657802,0.001369596,0.002873738,0.00512048,0.00157343,0.003375586,0.0005767541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001961909,"about_ca_system_score_gemma":0.001788133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002803536,"about_ca_topic_score_gemma":0.001777213,"domain_scores_codex":[0.9983552,0.0004965012,0.0000929262,0.0002907148,0.0005498942,0.0002148019],"domain_scores_gemma":[0.9942876,0.003026217,0.001019473,0.0005541238,0.0007744415,0.0003382147],"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":[0.000003201482,0.00001160187,0.0001288582,0.00001496955,0.000003089421,0.00005037438,0.00004273555,0.006093612,0.0002163962,0.9917835,0.0002346789,0.001416992],"study_design_scores_gemma":[0.000009287815,0.00003182714,0.0001877199,0.00002129372,0.00001724863,0.0001081171,0.00004093894,0.1604544,0.0004797773,0.8325542,0.006072391,0.00002279513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01532417,0.001163957,0.9630747,0.001606703,0.0004401061,0.00005114977,0.0001545675,0.000138224,0.01804637],"genre_scores_gemma":[0.7995254,0.004361764,0.1688456,0.0008726019,0.002008139,0.000252721,0.0002325761,0.0001821825,0.02371906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005962691,"threshold_uncertainty_score":0.01994723,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2979214939","doi":"10.1214/19-ejs1613","title":"Bootstrapping the empirical distribution of a stationary process with change-point","year":2019,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Mathematics; Bootstrapping (finance); Point process; Econometrics; Distribution (mathematics); Empirical distribution function; Statistics; Mathematical analysis","authors":[{"name":"Farid El Ktaibi","is_ca":true},{"name":"B. Gail Ivanoff","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1313243100196726,"gpt":0.3866303795236377,"spread":0.2553060695039651,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01284963,0.0005825448,0.00128901,0.002057162,0.0005821504,0.001041881,0.001615317,0.001068407,0.002572407],"category_scores_gemma":[0.07669305,0.0003399252,0.0008244877,0.001222673,0.002288495,0.002048853,0.001618883,0.001918055,0.0006683866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005142473,"about_ca_system_score_gemma":0.0009931836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001151906,"about_ca_topic_score_gemma":0.0008574561,"domain_scores_codex":[0.9969599,0.001592776,0.0001245973,0.0004758175,0.0007057435,0.000141257],"domain_scores_gemma":[0.9665464,0.02656265,0.001492372,0.003582185,0.00149913,0.000317227],"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":[0.001141205,0.0005654484,0.0399788,0.0006731639,0.0006387696,0.001144057,0.001262891,0.2044156,0.02359101,0.3934159,0.002508295,0.3306648],"study_design_scores_gemma":[0.00005640286,0.0004128261,0.01244073,0.000116353,0.00007930514,0.0004039472,0.0001806178,0.7989103,0.009137329,0.1752333,0.002968914,0.00006003945],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05559967,0.0001248041,0.9427011,0.000120786,0.00003783384,0.00010648,0.00005557444,0.0001673817,0.00108632],"genre_scores_gemma":[0.7204192,0.0003255765,0.277014,0.0001077618,0.0001125668,0.0003421843,0.0004996716,0.0001260065,0.001053128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01284963,"threshold_uncertainty_score":0.06795615,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3204801121","doi":"10.1214/22-ejs2007","title":"Smooth bootstrapping of copula functionals","year":2022,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Karlsruhe Institute of Technology","keywords":"Copula (linguistics); Mathematics; Estimator; Smoothing; Bootstrapping (finance); Kernel density estimation; Econometrics; Applied mathematics; Bounded function; Statistics; Mathematical analysis","authors":[{"name":"Maximilian Coblenz","is_ca":false},{"name":"Oliver Grothe","is_ca":false},{"name":"Klaus Herrmann","is_ca":true},{"name":"Marius Hofert","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06740254063029565,"gpt":0.3468231518855033,"spread":0.2794206112552077,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01001739,0.0009957236,0.00129547,0.002051204,0.0007056342,0.001617893,0.001600367,0.001251775,0.002570252],"category_scores_gemma":[0.05740922,0.0006361715,0.00125793,0.001809223,0.002078623,0.002162867,0.001960476,0.002469078,0.0006871917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007072543,"about_ca_system_score_gemma":0.0009383752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00163936,"about_ca_topic_score_gemma":0.001241027,"domain_scores_codex":[0.9965317,0.002459249,0.0000972595,0.0003110654,0.0004532795,0.0001473865],"domain_scores_gemma":[0.9695516,0.02439575,0.001214944,0.003130389,0.001333302,0.0003739492],"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":[0.0002373437,0.0001133589,0.004310658,0.0004207829,0.0003051735,0.0005991229,0.0004204725,0.3424405,0.00476328,0.5215619,0.004578823,0.1202486],"study_design_scores_gemma":[0.00001599329,0.00005188467,0.001008433,0.00005843684,0.00002366118,0.00008634463,0.00005467517,0.7828864,0.001075807,0.2127645,0.001946572,0.00002729045],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0117138,0.0003687686,0.9864748,0.0001930322,0.00004127908,0.00003921149,0.00004943852,0.0001439979,0.0009757618],"genre_scores_gemma":[0.5678875,0.001448032,0.4257962,0.0002845914,0.0002088628,0.0003688893,0.0005227191,0.0004620965,0.003021113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01001739,"threshold_uncertainty_score":0.05297762,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293508040","doi":"10.1214/22-ejs2035","title":"Improved estimation in tensor regression with multiple change-points","year":2022,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Tensor decomposition and applications","field":"Mathematics","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 Windsor","funders":"","keywords":"Mathematics; Estimator; Tensor (intrinsic definition); Context (archaeology); Applied mathematics; Regression; Statistics; Pure mathematics","authors":[{"name":"Mai Ghannam","is_ca":true},{"name":"Sévérien Nkurunziza","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02566141488805418,"gpt":0.308099937728837,"spread":0.2824385228407828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01190916,0.001483957,0.001908098,0.001368565,0.0004869786,0.001635055,0.001900794,0.002081357,0.001591582],"category_scores_gemma":[0.0413384,0.001044304,0.001538558,0.001651098,0.002013463,0.004936812,0.002220855,0.003083427,0.0006763896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008158509,"about_ca_system_score_gemma":0.001114802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002906835,"about_ca_topic_score_gemma":0.001896096,"domain_scores_codex":[0.9956284,0.002354868,0.0002241377,0.0009462409,0.0006385339,0.0002078827],"domain_scores_gemma":[0.9815047,0.01147952,0.002098578,0.002498548,0.002025472,0.000393144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004216573,0.0001294374,0.007407748,0.0006131077,0.0003857808,0.0006410526,0.0004945658,0.549868,0.01252817,0.2525738,0.002805961,0.1721306],"study_design_scores_gemma":[0.00001150771,0.00005380612,0.0007760235,0.00002191782,0.00003396168,0.00007833846,0.00001707147,0.9534369,0.001280009,0.04313162,0.001125367,0.00003339143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007697125,0.0002815594,0.9914308,0.0001656453,0.00002806167,0.00001363184,0.0000338135,0.0001139733,0.0002353587],"genre_scores_gemma":[0.3794335,0.001807607,0.6106234,0.0003530744,0.0005244188,0.000180766,0.0006087473,0.0003947878,0.006073602],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01190916,"threshold_uncertainty_score":0.06298238,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}