{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":15,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":15,"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":"b989878223e3","filters":{"venue":"Econometrics and Statistics"}},"results":[{"id":"W2612605702","doi":"10.1016/j.ecosta.2017.05.001","title":"A mixture of SDB skew- t factor analyzers","year":2017,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Actua; University of Waterloo; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Mixture model; Factor (programming language); sort; Extension (predicate logic); Expectation–maximization algorithm","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.0412038798764564,"gpt":0.289322261524362,"spread":0.2481183816479056,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008219483,0.001488476,0.001761295,0.003115507,0.001321346,0.004811832,0.002184342,0.002124477,0.01191132],"category_scores_gemma":[0.03010914,0.001488978,0.002249163,0.003891662,0.001882309,0.006032518,0.003622291,0.00298405,0.005114411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009943874,"about_ca_system_score_gemma":0.001827377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002406277,"about_ca_topic_score_gemma":0.002392551,"domain_scores_codex":[0.9943359,0.002202748,0.0004100196,0.001322117,0.001325062,0.0004040166],"domain_scores_gemma":[0.9900498,0.00384885,0.000540958,0.002432917,0.002614818,0.0005127087],"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":[0.001153485,0.0002178477,0.00859254,0.0002195277,0.0003682606,0.0003483706,0.0006590756,0.05186485,0.01941699,0.405515,0.007092542,0.5045515],"study_design_scores_gemma":[0.00006878845,0.0001111962,0.001944449,0.00007271428,0.00009006664,0.0005698917,0.000165207,0.6730696,0.005524001,0.3100458,0.008215507,0.0001227711],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006423769,0.0001138143,0.991748,0.0001304449,0.00003512048,0.00003952138,0.000093618,0.0003738044,0.001041885],"genre_scores_gemma":[0.1308022,0.0004083747,0.8599768,0.0002998525,0.0001292274,0.0002198001,0.0006991634,0.0005447004,0.006919821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01191132,"threshold_uncertainty_score":0.04346931,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3007603377","doi":"10.1016/j.ecosta.2020.01.005","title":"Flexible copula models with dynamic dependence and application to financial data","year":2020,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":false,"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":"Copula (linguistics); Multivariate statistics; Econometrics; Tail dependence; Gaussian; Factor analysis; Marginal distribution; Mathematics; Multivariate normal distribution; Conditional probability distribution; Latent variable; Computer science; Statistics; Random variable","authors":[{"name":"Pavel Krupskii","is_ca":true},{"name":"Harry Joe","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08610580707615353,"gpt":0.2550074269204539,"spread":0.1689016198443003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006696018,0.001532503,0.002730833,0.001941002,0.0008990322,0.003214051,0.003588163,0.002782386,0.003409514],"category_scores_gemma":[0.04017519,0.00217313,0.002465954,0.003812304,0.001819758,0.004496367,0.002686831,0.004883243,0.000794897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001396537,"about_ca_system_score_gemma":0.001513909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01194586,"about_ca_topic_score_gemma":0.008646683,"domain_scores_codex":[0.9979774,0.001052122,0.0001270298,0.000349308,0.0002906206,0.0002034488],"domain_scores_gemma":[0.9801642,0.01454929,0.001706847,0.00201817,0.001020582,0.0005408378],"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":[0.00005175442,0.00008182778,0.001532643,0.00009898782,0.0001678297,0.0003275238,0.0001596668,0.8066019,0.0005633515,0.1686939,0.002676917,0.01904377],"study_design_scores_gemma":[0.000004329153,0.000004353548,0.0001413673,0.000005897843,0.00000681826,0.00002277127,0.000005516064,0.9795715,0.00003435911,0.01993095,0.0002633338,0.000008804202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02616453,0.001262174,0.9698191,0.0006171927,0.00009895657,0.00004828934,0.0003524849,0.0004247823,0.001212494],"genre_scores_gemma":[0.7468065,0.003605583,0.2337629,0.0003830614,0.0005626327,0.0004348964,0.001801909,0.0009686459,0.01167386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01194586,"threshold_uncertainty_score":0.03541231,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2549940642","doi":"10.1016/j.ecosta.2016.10.004","title":"Identifying gene-environment interactions for prognosis using a robust approach","year":2016,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"National Cancer Institute","keywords":"Coordinate descent; Computer science; Accelerated failure time model; Quantile; Mixture model; Consistency (knowledge bases); Expectation–maximization algorithm; Stability (learning theory); Data mining; Quantile regression; Statistics; Mathematics; Algorithm; Covariate; Machine learning; Artificial intelligence; Maximum likelihood","authors":[{"name":"Hao Chai","is_ca":false},{"name":"Qingzhao Zhang","is_ca":false},{"name":"Yu Jiang","is_ca":false},{"name":"Guohua Wang","is_ca":false},{"name":"Sanguo Zhang","is_ca":false},{"name":"S. Ejaz Ahmed","is_ca":true},{"name":"Shuangge Ma","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1127961068007003,"gpt":0.2945561317281314,"spread":0.1817600249274312,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01503185,0.001117005,0.00311386,0.002400457,0.0005607338,0.001636257,0.002187523,0.001788016,0.002590353],"category_scores_gemma":[0.03456533,0.0008894395,0.00343617,0.001641452,0.00128138,0.001254002,0.001591199,0.001976524,0.0007682179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007168504,"about_ca_system_score_gemma":0.002011299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004924557,"about_ca_topic_score_gemma":0.004083898,"domain_scores_codex":[0.9933962,0.004353871,0.0003312896,0.001043242,0.0005368533,0.0003385885],"domain_scores_gemma":[0.9699093,0.0252178,0.001695711,0.002129296,0.0007078599,0.0003399898],"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":[0.0009741472,0.0004966374,0.04012526,0.0002083913,0.003470666,0.000557599,0.0001021812,0.7413832,0.005794893,0.02339183,0.002987157,0.180508],"study_design_scores_gemma":[0.00006299563,0.0001697243,0.005285802,0.00001176282,0.0002393327,0.00008106059,0.0000231104,0.972693,0.0006779147,0.02027001,0.0004524095,0.00003286203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02640875,0.0004191274,0.9715419,0.0005687242,0.00003586336,0.00004757675,0.0003226548,0.0004384393,0.0002168532],"genre_scores_gemma":[0.7896127,0.0004556342,0.2053139,0.0003851363,0.0002731621,0.0002191568,0.001394375,0.0001883748,0.002157504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01503185,"threshold_uncertainty_score":0.07949692,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4366977409","doi":"10.1016/j.ecosta.2023.04.004","title":"Robust nonparametric regression: Review and practical considerations","year":2023,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Advanced Statistical Methods and Models","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric regression; Nonparametric statistics; Outlier; Robust regression; Estimator; Covariate; Regression analysis; Econometrics; Regression; Regression diagnostic; Semiparametric regression; Statistics; Computer science; Mathematics; Polynomial regression","authors":[{"name":"Matías Salibián‐Barrera","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4005423771412275,"gpt":0.4695493889941788,"spread":0.06900701185295133,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004900785,0.001202496,0.002273085,0.003123123,0.0004050923,0.001911599,0.002756891,0.002472852,0.003363095],"category_scores_gemma":[0.01256872,0.0008728648,0.001085661,0.005676547,0.001806591,0.002725873,0.001033528,0.003087449,0.003074148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215032,"about_ca_system_score_gemma":0.001798264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002601255,"about_ca_topic_score_gemma":0.001423273,"domain_scores_codex":[0.9980192,0.0007309128,0.000240961,0.0003762492,0.0005719691,0.00006063483],"domain_scores_gemma":[0.9911348,0.006403391,0.0004906862,0.0003064636,0.001543861,0.0001207823],"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":[0.00007418152,0.00007888836,0.000427112,0.01679366,0.0001951376,0.0002740204,0.0001210917,0.006730749,0.0006132295,0.08975893,0.06408123,0.8208517],"study_design_scores_gemma":[0.00001527858,0.0001011117,0.001222624,0.006971193,0.0001480141,0.001026561,0.00008409128,0.003407414,0.0004200222,0.05198184,0.9345309,0.00009085985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.0001371348,0.9833543,0.0120477,0.001542842,0.0006894104,0.0000130952,0.0000597474,0.0000428405,0.002112858],"genre_scores_gemma":[0.002788375,0.9874182,0.005944948,0.0007967505,0.002064968,0.00003857904,0.0001302105,0.00003417389,0.0007837744],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004900785,"threshold_uncertainty_score":0.02591813,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3209165885","doi":"10.1016/j.ecosta.2021.10.015","title":"A Markov decision process for response adaptive designs","year":2021,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov decision process; Mathematical optimization; Markov chain; Markov process; Operator (biology); Process (computing); Partially observable Markov decision process; Mathematics; Markov model; Value (mathematics); Computer science; Statistics","authors":[{"name":"Yanqing Yi","is_ca":true},{"name":"Xikui Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.7140437471766378,"gpt":0.566349235593182,"spread":0.1476945115834558,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03985193,0.002264892,0.004296863,0.002160518,0.001043373,0.002642222,0.004236429,0.004403891,0.01069295],"category_scores_gemma":[0.09545239,0.002542887,0.00306553,0.002562128,0.003825926,0.004384609,0.003897168,0.006641843,0.001919447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002401478,"about_ca_system_score_gemma":0.005055482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003962054,"about_ca_topic_score_gemma":0.002783064,"domain_scores_codex":[0.9719491,0.02107706,0.0009989187,0.002677822,0.002496074,0.0008009973],"domain_scores_gemma":[0.8806616,0.1086067,0.003045317,0.003544295,0.003168453,0.0009735209],"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":[0.0003080388,0.00009386944,0.0006932686,0.000251216,0.0001889815,0.0001613967,0.0001774404,0.1821186,0.0006131444,0.7771164,0.002483286,0.03579446],"study_design_scores_gemma":[0.0001816434,0.0001345927,0.0002064307,0.00007681728,0.00007447893,0.00008906568,0.00001415378,0.5938166,0.0002186885,0.4030471,0.002095395,0.00004494296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001025342,0.0001921116,0.9978453,0.000277679,0.00004116292,0.00007344342,0.00006680165,0.000073216,0.0004049575],"genre_scores_gemma":[0.1703824,0.001740543,0.8155877,0.0009554013,0.000493076,0.002420719,0.0007078204,0.0002087325,0.007503566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03985193,"threshold_uncertainty_score":0.2107596,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3197157960","doi":"10.1016/j.ecosta.2021.11.009","title":"Fast cluster bootstrap methods for linear regression models","year":2021,"lang":"en","type":"preprint","venue":"Econometrics and Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Bootstrapping (finance); Monte Carlo method; Cluster (spacecraft); Linear regression; Computation; Ordinary least squares; Regression; Regression analysis; Cluster analysis; Mathematics; Algorithm; Computer science; Statistics; Econometrics","authors":[{"name":"James G. MacKinnon","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3369673694098759,"gpt":0.5058187816939906,"spread":0.1688514122841148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006860194,0.001551191,0.002025817,0.003145402,0.001307838,0.002051943,0.004462847,0.002217459,0.01049714],"category_scores_gemma":[0.04466689,0.001374232,0.001973562,0.004147904,0.001522762,0.00274224,0.002724675,0.004654544,0.005356191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001263531,"about_ca_system_score_gemma":0.00222988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006974425,"about_ca_topic_score_gemma":0.007716087,"domain_scores_codex":[0.9949708,0.003284371,0.0001607196,0.0004335942,0.0009586602,0.0001918573],"domain_scores_gemma":[0.97752,0.01590654,0.0006277031,0.003061823,0.002482308,0.0004015086],"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":[0.0004792688,0.0002311066,0.001216374,0.0008072184,0.0005304329,0.0002326268,0.0003542724,0.2654521,0.003829135,0.3771896,0.03355615,0.3161218],"study_design_scores_gemma":[0.00005380862,0.00002161454,0.0003060754,0.00004909129,0.00003681901,0.00005008226,0.00002626818,0.7969969,0.0009562935,0.193653,0.007820173,0.00002991955],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009840311,0.000290354,0.9973167,0.00009108181,0.00006706683,0.00002678135,0.0001252608,0.0006272173,0.0004715408],"genre_scores_gemma":[0.06700361,0.0010884,0.9190369,0.0002252404,0.0005042232,0.0007061171,0.00163755,0.002328316,0.007469668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01049714,"threshold_uncertainty_score":0.03628063,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387358801","doi":"10.1016/j.ecosta.2023.09.001","title":"Robust nonparametric multiple changepoint detection for multivariate variability","year":2023,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric statistics; Multivariate statistics; Robustness (evolution); Algorithm; Thresholding; Statistic; Mathematics; Univariate; Outlier; Computer science; Pattern recognition (psychology); Statistics; Artificial intelligence","authors":[{"name":"Kelly Ramsay","is_ca":true},{"name":"Shojaeddin Chenouri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2746445275155663,"gpt":0.3929982376320603,"spread":0.118353710116494,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005664188,0.001043776,0.001856072,0.002067053,0.0005303819,0.001928835,0.00228314,0.001589286,0.002276693],"category_scores_gemma":[0.03878263,0.0007972578,0.001453554,0.002351448,0.001502018,0.002355044,0.002626838,0.003111964,0.0007760922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006855711,"about_ca_system_score_gemma":0.001136581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134682,"about_ca_topic_score_gemma":0.001213821,"domain_scores_codex":[0.9953095,0.001840591,0.0002169225,0.001166766,0.001213628,0.0002525293],"domain_scores_gemma":[0.9802991,0.01421513,0.001828436,0.002147789,0.001271517,0.0002379662],"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":[0.0007744333,0.0002085176,0.004121451,0.0004617935,0.0005118208,0.0003238487,0.0002524529,0.2619098,0.02032218,0.1442141,0.004435146,0.5624644],"study_design_scores_gemma":[0.00002298439,0.00008874224,0.001671237,0.00002824097,0.00003636789,0.0001642093,0.00002065102,0.9194005,0.004175523,0.07239922,0.001953128,0.00003930953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003464183,0.00009910716,0.9959096,0.00005493607,0.00001834452,0.00001139442,0.00003755411,0.0001944676,0.000210478],"genre_scores_gemma":[0.4055067,0.0004951085,0.5880311,0.0001432837,0.0002424004,0.0002359907,0.0006845632,0.0005172366,0.00414367],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005664188,"threshold_uncertainty_score":0.02995545,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3208423201","doi":"10.1016/j.ecosta.2021.10.011","title":"Multivariate time-series modeling with generative neural networks","year":2021,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"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":"Copula (linguistics); Autoregressive conditional heteroskedasticity; Econometrics; Univariate; Marginal distribution; Multivariate statistics; Principal component analysis; Series (stratigraphy); Joint probability distribution; Computer science; Time series; Mathematics; Statistics; Random variable; Volatility (finance)","authors":[{"name":"Marius Hofert","is_ca":true},{"name":"Avinash Prasad","is_ca":true},{"name":"Mu Zhu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1407767531817722,"gpt":0.3582987306385353,"spread":0.2175219774567631,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001192091,0.000626255,0.0008439023,0.0006885818,0.0003226127,0.001032304,0.001410866,0.001432581,0.001932482],"category_scores_gemma":[0.00578892,0.0008780651,0.00120082,0.0009081751,0.0009128537,0.001278474,0.001130904,0.001756988,0.0004139462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007446294,"about_ca_system_score_gemma":0.0005447351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00782756,"about_ca_topic_score_gemma":0.007742502,"domain_scores_codex":[0.9995881,0.000193941,0.00002159939,0.00009113632,0.0000703133,0.0000349557],"domain_scores_gemma":[0.9974789,0.001949415,0.0002244912,0.0001426713,0.0001442054,0.00006028164],"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":[0.0000205676,0.00001330656,0.000424885,0.00002238043,0.00003995214,0.00003223375,0.00003079194,0.9658767,0.0003096803,0.02264634,0.0002738714,0.01030938],"study_design_scores_gemma":[0.000001570155,0.000001420878,0.00003998323,0.00000220659,0.000003092003,0.000004638293,0.000001150345,0.9924433,0.00003559266,0.007406622,0.00005814193,0.000002291615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01499942,0.0004489509,0.9827951,0.0002747173,0.00007569027,0.00001677123,0.00008695645,0.0002509535,0.001051448],"genre_scores_gemma":[0.8574551,0.00117949,0.1339248,0.0002208744,0.0002430622,0.0001499508,0.0003323759,0.0001706515,0.006323761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00782756,"threshold_uncertainty_score":0.01556396,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2794953498","doi":"10.1016/j.ecosta.2019.01.003","title":"A general white noise test based on kernel lag-window estimates of the spectral density operator","year":2019,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Fédération Wallonie-Bruxelles","keywords":"Estimator; Spectral density; Test statistic; White noise; Kernel (algebra); Series (stratigraphy); Variable kernel density estimation; Kernel density estimation; Statistic; Operator (biology)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02044530303228892,"gpt":0.2075704172328946,"spread":0.1871251142006057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008526225,0.0009530953,0.001847506,0.002480165,0.0004924512,0.001574946,0.002045489,0.002208846,0.005710048],"category_scores_gemma":[0.06121512,0.0004935979,0.001032802,0.00177636,0.001665259,0.003888658,0.001887229,0.001239857,0.0009842414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004592619,"about_ca_system_score_gemma":0.00152253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009803888,"about_ca_topic_score_gemma":0.0009158991,"domain_scores_codex":[0.9954689,0.002163777,0.0002922298,0.00117606,0.0006791925,0.0002198826],"domain_scores_gemma":[0.9467744,0.04484622,0.00226162,0.002854275,0.002446631,0.000816856],"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":[0.004496683,0.0009388577,0.04037526,0.0007534328,0.001430888,0.0009853413,0.0003183458,0.2545144,0.03903545,0.1967873,0.00489865,0.4554654],"study_design_scores_gemma":[0.0002259257,0.000590571,0.008691979,0.00004442238,0.0001462175,0.0004070215,0.00006441124,0.926858,0.004926763,0.05677092,0.001175595,0.00009823865],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1044544,0.0003301807,0.8920225,0.0002395648,0.0001462849,0.00008444145,0.0002558688,0.0006963318,0.001770525],"genre_scores_gemma":[0.7862018,0.000333172,0.2075747,0.0002279926,0.0003571184,0.000184983,0.001348697,0.000235503,0.003535968],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008526225,"threshold_uncertainty_score":0.04509151,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3008164135","doi":"10.1016/j.ecosta.2020.01.002","title":"Bootstrap seasonal unit root test under periodic variation","year":2020,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Unit root; Unit root test; Statistics; Mathematics; Seasonality; Variation (astronomy); Test (biology); Econometrics; Environmental science; Biology; Ecology; Physics; Cointegration","authors":[{"name":"Nan Zou","is_ca":true},{"name":"Dimitris N. Politis","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1566101831742532,"gpt":0.2452604128991585,"spread":0.08865022972490527,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014303,0.0005314757,0.001789741,0.001548569,0.0009558977,0.001173033,0.00215679,0.001873979,0.01284153],"category_scores_gemma":[0.0929455,0.0005659558,0.001193273,0.001353984,0.001458826,0.002137118,0.001155831,0.001541451,0.001238937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004960601,"about_ca_system_score_gemma":0.001211196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002079127,"about_ca_topic_score_gemma":0.001992502,"domain_scores_codex":[0.9936493,0.003615977,0.0002666362,0.00125838,0.0008186036,0.0003910637],"domain_scores_gemma":[0.891676,0.09146143,0.003601243,0.008856713,0.003188289,0.001216345],"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":[0.006226826,0.00110503,0.1206338,0.0006430796,0.002658196,0.003071057,0.0009988835,0.2757417,0.01118331,0.1435928,0.02159125,0.4125542],"study_design_scores_gemma":[0.0002786526,0.0005995717,0.02728688,0.00005702536,0.0001933077,0.0004645785,0.0003311596,0.9234073,0.002464856,0.04242934,0.002435703,0.00005157844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5414382,0.0004642718,0.4489533,0.0006535096,0.0003007181,0.0001544881,0.0009970659,0.001457087,0.005581324],"genre_scores_gemma":[0.958686,0.00009055834,0.03684371,0.0001322092,0.000180574,0.0001037065,0.001674303,0.0002279652,0.002061053],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.014303,"threshold_uncertainty_score":0.07564241,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3213651867","doi":"10.1016/j.ecosta.2021.10.010","title":"GMM with Nearly-Weak Identification","year":2021,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Jacobian matrix and determinant; Estimator; Mathematics; Generalized method of moments; A priori and a posteriori; Applied mathematics; Convergence (economics); Inference; Identification (biology); Matrix (chemical analysis); Space (punctuation); Inverse; Computer science; Statistics; Artificial intelligence; Geometry","authors":[{"name":"Bertille Antoine","is_ca":true},{"name":"Éric Renault","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07304404295056509,"gpt":0.2173536498676374,"spread":0.1443096069170723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01410287,0.001662187,0.003095804,0.001542146,0.001006893,0.003025192,0.002189161,0.003072737,0.006709585],"category_scores_gemma":[0.05450165,0.001757459,0.001981902,0.00158967,0.001571345,0.004268796,0.004148371,0.002948203,0.004221587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000630008,"about_ca_system_score_gemma":0.00202689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00470989,"about_ca_topic_score_gemma":0.005126092,"domain_scores_codex":[0.9925643,0.004847023,0.0005240971,0.001051424,0.0005400011,0.0004732743],"domain_scores_gemma":[0.9652019,0.02484314,0.00135045,0.006264724,0.001931965,0.0004078564],"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":[0.0009370235,0.0002430657,0.01781525,0.001099625,0.001937355,0.001669758,0.0006170673,0.3663843,0.004240328,0.3707538,0.03736991,0.1969324],"study_design_scores_gemma":[0.0001415489,0.00008873345,0.004685001,0.00007595386,0.0002520764,0.0004473586,0.0001228392,0.7248323,0.002179848,0.2584536,0.008646903,0.0000739851],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03037291,0.001112764,0.9559425,0.002954406,0.0004919885,0.00009188491,0.001008412,0.001631653,0.006393522],"genre_scores_gemma":[0.716387,0.001617813,0.2419977,0.002407469,0.00151562,0.0003675069,0.004958669,0.0009216585,0.02982664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01410287,"threshold_uncertainty_score":0.07458401,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4384945676","doi":"10.1016/j.ecosta.2023.07.004","title":"Estimation of Extreme Risk Measures for Stochastic Volatility Models with Long Memory and Heavy Tails","year":2023,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Stochastic volatility; Econometrics; Long memory; Volatility (finance); Value at risk; Economics; Extreme value theory; Mathematics; Statistics; Risk management; Finance","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":0.07386067975230526,"gpt":0.2398231404937475,"spread":0.1659624607414422,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005750323,0.0009956999,0.001540637,0.001604121,0.0003840857,0.001917721,0.00185781,0.001637207,0.001003631],"category_scores_gemma":[0.0277499,0.001106129,0.001572193,0.0008931065,0.001050114,0.003203446,0.00231806,0.002297501,0.0002074716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005793008,"about_ca_system_score_gemma":0.001127151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001433121,"about_ca_topic_score_gemma":0.001051175,"domain_scores_codex":[0.9984712,0.0009014899,0.00009258898,0.0001989211,0.000214197,0.0001214748],"domain_scores_gemma":[0.9828897,0.01429503,0.001254041,0.0006375924,0.000622145,0.0003014249],"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":[0.0001095579,0.0001271066,0.004519686,0.0001156928,0.0002903029,0.0001239596,0.0001146678,0.8676155,0.001604606,0.08411668,0.0006638729,0.04059838],"study_design_scores_gemma":[0.000007055602,0.00001915325,0.0003469349,0.00001049717,0.00001332086,0.00002388717,0.000008404168,0.9718304,0.0003077415,0.02731938,0.0001028479,0.00001034253],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03370273,0.0002725619,0.9655326,0.00008830294,0.00001470781,0.00001773725,0.00003143504,0.00008494295,0.0002550833],"genre_scores_gemma":[0.7994496,0.0008622723,0.1969006,0.00008715762,0.0001465381,0.0001738369,0.0005587788,0.0001299021,0.001691427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005750323,"threshold_uncertainty_score":0.03041101,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408520359","doi":"10.1016/j.ecosta.2025.03.002","title":"Estimation of multifactor stochastic volatility jump-diffusion models: A marginalized filter approach","year":2025,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":1,"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; Alliance de recherche numérique du Canada; Simon Fraser University","keywords":"Jump diffusion; Stochastic volatility; Econometrics; Jump; Volatility (finance); Mathematics; Estimation; Economics; Computer science; Statistical physics; Physics","authors":[{"name":"Jean‐François Bégin","is_ca":true},{"name":"Golara Zafari","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04956406265868604,"gpt":0.244745794058517,"spread":0.195181731399831,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002124133,0.0006619791,0.001027058,0.0007328014,0.0003171231,0.001087522,0.001404535,0.001279126,0.002062055],"category_scores_gemma":[0.005383863,0.0006562624,0.001410883,0.0007017496,0.0006358315,0.001640708,0.001279702,0.001574557,0.0004072065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008274899,"about_ca_system_score_gemma":0.001603852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00652029,"about_ca_topic_score_gemma":0.004727395,"domain_scores_codex":[0.9992442,0.0002879353,0.00004562498,0.0001863219,0.0001737197,0.00006220621],"domain_scores_gemma":[0.9982111,0.001206586,0.0001803614,0.0001502602,0.0002009952,0.00005062973],"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":[0.00008679076,0.00006731095,0.001529016,0.0001153329,0.0001698895,0.0001011578,0.00009606367,0.8122072,0.005102361,0.08736689,0.0008798324,0.09227818],"study_design_scores_gemma":[0.000003591904,0.00001142043,0.0001469477,0.000004300452,0.000007433321,0.00001033416,0.000002469744,0.9923713,0.0003212778,0.006736977,0.0003757419,0.00000828537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002691799,0.00006684168,0.9969143,0.00004897826,0.000009743792,0.000006698383,0.00002237061,0.00006500904,0.0001743458],"genre_scores_gemma":[0.3617896,0.0007253485,0.6327138,0.0001594758,0.0001435045,0.0001770392,0.000432964,0.0001197273,0.003738572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00652029,"threshold_uncertainty_score":0.01296473,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963821891","doi":"10.1016/j.ecosta.2019.07.002","title":"Introduction to the special topic on copula modeling","year":2019,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Copula (linguistics); Econometrics; Computer science; Mathematical economics; Mathematics","authors":[{"name":"Christian Genest","is_ca":true},{"name":"Ivan Kojadinovic","is_ca":false},{"name":"Fabrizio Durante","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03749166058196539,"gpt":0.2214556984653195,"spread":0.1839640378833541,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002112459,0.002162549,0.002509912,0.00309801,0.0006856482,0.003256169,0.001708761,0.003391956,0.02665169],"category_scores_gemma":[0.006094667,0.0009725828,0.002796277,0.004064646,0.001515027,0.004274962,0.002145031,0.008502905,0.01645774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001056202,"about_ca_system_score_gemma":0.0009652522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007714964,"about_ca_topic_score_gemma":0.0008180232,"domain_scores_codex":[0.9987561,0.000340513,0.0001219812,0.000385206,0.0003279538,0.00006816212],"domain_scores_gemma":[0.9954013,0.002986908,0.0001545125,0.0004080183,0.0007699265,0.0002793898],"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":[0.00005054281,0.000232559,0.0004935959,0.001350179,0.00013702,0.0004088122,0.0001617793,0.00493958,0.001858652,0.2266413,0.5499615,0.2137645],"study_design_scores_gemma":[0.00002036807,0.00007968924,0.000850141,0.0005814487,0.00005080557,0.0007097973,0.00003546909,0.008905081,0.0003904345,0.2241166,0.7641834,0.00007676111],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.001519662,0.2481909,0.5076423,0.02983978,0.1280247,0.0001822224,0.001563628,0.00155582,0.08148106],"genre_scores_gemma":[0.02396054,0.2288748,0.1507864,0.02810953,0.453372,0.0004879416,0.002488502,0.002334956,0.1095853],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.02665169,"threshold_uncertainty_score":0.08915877,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385776757","doi":"10.1016/j.ecosta.2023.08.001","title":"A computationally efficient mixture innovation model for time-varying parameter regressions","year":2023,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Mixture model; Block (permutation group theory); Computer science; Latent variable; Computation; Algorithm; Bayesian probability; Markov chain Monte Carlo; Statistics; Mathematics; Econometrics; Mathematical optimization","authors":[{"name":"Zhongfang He","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05884570760446368,"gpt":0.3058741556124592,"spread":0.2470284480079955,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004742388,0.001032059,0.001869582,0.001436876,0.0007694422,0.001761724,0.004152081,0.002431726,0.005627613],"category_scores_gemma":[0.01412678,0.001259054,0.002080717,0.002172479,0.001437404,0.002733376,0.002043824,0.003931088,0.001982317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001605161,"about_ca_system_score_gemma":0.002020563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01167224,"about_ca_topic_score_gemma":0.01029437,"domain_scores_codex":[0.997917,0.001056333,0.00007985755,0.0003323101,0.0004215151,0.0001930612],"domain_scores_gemma":[0.995384,0.003421123,0.0003355716,0.0002648579,0.0004573459,0.00013709],"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":[0.0001074995,0.00004896166,0.001235579,0.0001112043,0.0000861525,0.0001267342,0.0001681179,0.6758372,0.001422222,0.2694604,0.002247271,0.04914863],"study_design_scores_gemma":[0.000009247504,0.00001074662,0.0001207083,0.000009476892,0.00001126512,0.0000235761,0.000006936822,0.9699019,0.0001825428,0.02840995,0.001297825,0.00001587825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002228042,0.0001195005,0.9967682,0.00010758,0.00001668945,0.00001852373,0.00005144867,0.0001454776,0.0005444873],"genre_scores_gemma":[0.2146882,0.0009957595,0.7699307,0.0002557207,0.0001535473,0.0005806437,0.0008726765,0.0004744017,0.01204832],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01167224,"threshold_uncertainty_score":0.02508044,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}