{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":40,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":40,"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"},"query_hash":"3f9eab64ec39","filters":{"venue":"Stats"}},"results":[{"id":"W3083104667","doi":"10.3390/stats3030023","title":"Improving Access to Justice with Legal Chatbots","year":2020,"lang":"en","type":"article","venue":"Stats","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"National Bank of Canada; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec-Société et Culture","keywords":"Dialog box; Public relations; Disadvantaged; Economic Justice; Chatbot; Immigration; Government (linguistics); Legal advice; Computer science; Political science; Internet privacy; Law; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.1303794885060638,"gpt":0.4101783170634904,"spread":0.2797988285574267,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001882574,0.00007827939,0.00009409784,0.00002249523,0.0004107748,0.0002906839,0.0004265643,0.00003618472,0.0003196602],"category_scores_gemma":[0.0004598172,0.00007050733,0.00001911271,0.0004168925,0.0001539806,0.0006021951,0.00009452099,0.0001014809,0.0003531538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000483214,"about_ca_system_score_gemma":0.0002103723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005085056,"about_ca_topic_score_gemma":0.007438531,"domain_scores_codex":[0.9988943,0.00005415858,0.0001204268,0.0002407259,0.0003520832,0.0003382458],"domain_scores_gemma":[0.9993526,0.00008196686,0.00004682631,0.0001011657,0.0001077586,0.000309695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005467181,0.0001244549,0.004530653,0.0001576425,0.00005474316,0.0002956578,0.2150406,0.002428066,0.005791714,0.394162,0.03463712,0.3422306],"study_design_scores_gemma":[0.0002177126,0.0007605837,0.001568198,0.00007803302,0.000129285,0.000002014558,0.05844893,0.001974628,0.02353614,0.00254308,0.9096703,0.001071119],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4997005,0.00006578871,0.1097478,0.08814641,0.001607974,0.001485188,0.00003273145,0.0006930301,0.2985206],"genre_scores_gemma":[0.9928825,0.000003957088,0.001420632,0.004494086,0.0006463545,0.00001264089,8.033034e-7,0.00001313024,0.0005258564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8750331,"threshold_uncertainty_score":0.7687118,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3096641854","doi":"10.3390/stats3040029","title":"Psychometric Properties of the Adult Self-Report: Data from over 11,000 American Adults","year":2020,"lang":"en","type":"article","venue":"Stats","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Children's Hospital of Eastern Ontario","funders":"Jenny ja Antti Wihurin Rahasto","keywords":"Measurement invariance; Structural equation modeling; Confirmatory factor analysis; Environmental scanning electron microscope; Psychology; Factor analysis; Exploratory factor analysis; Developmental psychology; Clinical psychology; Psychometrics; Statistics; Mathematics; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.1335417025263125,"gpt":0.3959762303929784,"spread":0.262434527866666,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000772108,0.000117455,0.0002071698,0.00005614992,0.00006263465,0.00001208657,0.0006362665,0.00004551218,0.001502337],"category_scores_gemma":[0.0001031986,0.00007851112,0.00008377498,0.0007356976,0.0001168585,0.0000982361,0.0001653866,0.0001829464,0.0001657263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001639352,"about_ca_system_score_gemma":0.00004746204,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01389178,"about_ca_topic_score_gemma":0.001147794,"domain_scores_codex":[0.9985691,0.0001045775,0.0004458024,0.0004054495,0.0002269747,0.0002481517],"domain_scores_gemma":[0.9984148,0.00002084578,0.0002989828,0.001021461,0.00009635141,0.0001475181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000615296,0.001222822,0.6258544,0.0001044052,0.0001676388,0.00002828839,0.00785472,3.825153e-7,0.0004167501,0.0002003578,0.2745996,0.08893535],"study_design_scores_gemma":[0.0009248694,0.0003361653,0.9552615,0.0001047739,0.00007385706,0.000005923847,0.001394715,0.00007320516,0.0002216703,0.00002175014,0.04142288,0.000158653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941186,0.0004561908,0.00002692514,0.001819931,0.0008572986,0.0003238185,0.001186438,0.00006854083,0.001142198],"genre_scores_gemma":[0.997897,0.00002330601,0.0001771023,0.001224357,0.0001446951,0.00002649153,0.0001050701,0.00001993244,0.0003820662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3294071,"threshold_uncertainty_score":0.9994105,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293104237","doi":"10.3390/stats5020019","title":"A Bootstrap Variance Estimation Method for Multistage Sampling and Two-Phase Sampling When Poisson Sampling Is Used at the Second Phase","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Poisson sampling; Sampling (signal processing); Sampling design; Stratified sampling; Statistics; Weighting; Variance (accounting); Multistage sampling; Cluster sampling; Poisson distribution; Slice sampling; Computer science; Simple random sample; Systematic sampling; Mathematics; Importance sampling; Monte Carlo method","retraction":null,"screen_n_in":null,"score":{"opus":0.2349896117496487,"gpt":0.5044269009477197,"spread":0.269437289198071,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002095285,0.0002852233,0.000441362,0.000082853,0.001109681,0.000169389,0.0002322999,0.00005791062,0.001585495],"category_scores_gemma":[0.001501254,0.0002448173,0.0001004978,0.0001608875,0.00008466416,0.0001372496,0.0002036798,0.0003318361,0.00000290092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001493624,"about_ca_system_score_gemma":0.00007174589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006907073,"about_ca_topic_score_gemma":0.00006784384,"domain_scores_codex":[0.9976619,0.0003329668,0.0005861107,0.0005812226,0.0003466688,0.0004910579],"domain_scores_gemma":[0.9902043,0.008789022,0.0003407179,0.0004330746,0.00008890041,0.0001439946],"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":[0.0009366508,0.0006493519,0.00006102495,0.000982411,0.0002195947,0.00001920385,0.01866974,0.0002732085,0.04672431,0.3122145,0.001302095,0.6179479],"study_design_scores_gemma":[0.004584503,0.0003374206,0.00003924114,0.00005800865,0.0001288332,0.00003485148,0.0006789453,0.1410207,0.002464059,0.8428932,0.007397431,0.0003627572],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02662585,0.0001127302,0.9677671,0.0004061397,0.0002065439,0.001059409,0.003652406,0.00008017656,0.00008963303],"genre_scores_gemma":[0.02145586,0.000003012296,0.9772617,0.0003851911,0.00007844843,0.0003103658,0.0000816389,0.00005905753,0.0003647916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6175852,"threshold_uncertainty_score":0.9993272,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3014530657","doi":"10.3390/stats3020008","title":"The Prediction of Batting Averages in Major League Baseball","year":2020,"lang":"en","type":"article","venue":"Stats","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Luck; League; Ball (mathematics); Econometrics; Component (thermodynamics); Computer science; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04301146415228031,"gpt":0.2128941975765797,"spread":0.1698827334242994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000233735,0.00004752912,0.0001212249,0.00003685942,0.00004793049,0.00001885603,0.00008903831,0.00002282684,0.0002199106],"category_scores_gemma":[0.00005142731,0.00004229065,0.00003347367,0.0001322022,0.0000220508,0.00007170188,0.00002132286,0.00007688574,0.00004151048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001466202,"about_ca_system_score_gemma":0.000008610159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001392719,"about_ca_topic_score_gemma":0.00004926592,"domain_scores_codex":[0.9994018,0.000002932324,0.0003340559,0.0001239095,0.00002187081,0.0001154073],"domain_scores_gemma":[0.9996865,0.00002927729,0.000145789,0.00009764087,0.00001146229,0.0000292679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003529266,0.00003369099,0.9294154,0.0000659947,0.00003354822,0.000005615257,0.001815612,0.002912892,0.0000540952,0.05652908,0.004642865,0.004455938],"study_design_scores_gemma":[0.0008196373,0.0001388565,0.6187401,0.00003044997,0.000004914889,8.426092e-7,0.0003635156,0.2284822,0.00049343,0.004856896,0.1458871,0.0001820797],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729224,0.001236913,0.001183319,0.002261415,0.0002945711,0.0001256774,0.0002381055,0.00001544989,0.02172211],"genre_scores_gemma":[0.9988755,0.0004042003,0.00005298983,0.0001648472,0.00006850465,0.000002941486,0.000007955558,0.000006385496,0.0004166771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3106753,"threshold_uncertainty_score":0.2407866,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3116487341","doi":"10.3390/stats4010002","title":"General Formulas for the Central and Non-Central Moments of the Multinomial Distribution","year":2021,"lang":"en","type":"article","venue":"Stats","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Multinomial distribution; Mathematics; Factorial; Distribution (mathematics); Order (exchange); L-moment; Central moment; Argument (complex analysis); Method of moments (probability theory); Applied mathematics; Mathematical analysis; Statistics; Random variable; Order statistic; Moment-generating function","retraction":null,"screen_n_in":null,"score":{"opus":0.01537497125506099,"gpt":0.2656864378694993,"spread":0.2503114666144383,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001355192,0.00006905106,0.00008643801,0.000004868903,0.0001355491,0.0000493249,0.0002513462,0.00002932711,0.000001782392],"category_scores_gemma":[0.00002971846,0.00003921673,0.00006895659,0.0000854217,0.00003415965,0.00009760562,0.0001569572,0.00005517187,1.284796e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002364227,"about_ca_system_score_gemma":0.00007678293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002233059,"about_ca_topic_score_gemma":0.00001367454,"domain_scores_codex":[0.9992954,0.00004778519,0.0001215125,0.0001642009,0.0001115709,0.0002595588],"domain_scores_gemma":[0.9995185,0.00007197176,0.00005378072,0.000256009,0.00005186664,0.00004787986],"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":[0.00007400278,0.0001593797,0.005189296,0.00008973369,0.0001236163,0.00000722602,0.003753909,0.0003056197,0.02033842,0.3232566,0.006859661,0.6398426],"study_design_scores_gemma":[0.002833282,0.0001062702,0.2862372,0.0000417818,0.00006139535,0.0000235607,0.00004556415,0.5232512,0.1274214,0.05122771,0.008441122,0.000309485],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06906357,0.00009226524,0.9292095,0.0006780344,0.0006157103,0.0002042445,0.00007629145,0.000006321864,0.00005402369],"genre_scores_gemma":[0.8598706,0.00001943849,0.1396789,0.0001178226,0.00009896809,0.00001076788,0.000008100013,0.000003700729,0.0001917726],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.790807,"threshold_uncertainty_score":0.1599212,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3042519163","doi":"10.3390/stats3030016","title":"Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method","year":2020,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian Inference","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":"Public Health Ontario; University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Mixed model; Pairwise comparison; Bivariate analysis; Quasi-maximum likelihood; Computer science; Statistics; Statistic; Inference; Multivariate analysis; Generalized linear mixed model; Statistical inference; Mathematics; Maximum likelihood; Artificial intelligence; Expectation–maximization algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.1033681661564572,"gpt":0.3912805883882589,"spread":0.2879124222318017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007550815,0.0002213795,0.0003857577,0.00003845388,0.00009338861,0.00004761766,0.0001962116,0.00006983525,0.00009592208],"category_scores_gemma":[0.001772633,0.0001651565,0.00005644144,0.0002166947,0.0000618226,0.00007517872,0.00007975052,0.000232773,0.00004012978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002785491,"about_ca_system_score_gemma":0.0001166613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001472208,"about_ca_topic_score_gemma":0.000004371464,"domain_scores_codex":[0.9979209,0.0007401367,0.0003059627,0.0003811372,0.0002936749,0.000358142],"domain_scores_gemma":[0.9962419,0.002926386,0.000114488,0.0002845851,0.0001153561,0.0003172483],"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":[0.01491491,0.0006418334,0.0003216551,0.0006114003,0.0003396128,0.0004679227,0.01231081,0.0003937879,0.08615436,0.7020338,0.009478589,0.1723313],"study_design_scores_gemma":[0.001309031,0.0004335347,0.0007579359,0.00006631934,0.00009419318,0.000008001816,0.0001619197,0.2858042,0.006290542,0.7045222,0.000199175,0.0003529285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01138524,0.000009615941,0.9854102,0.001702929,0.00003785449,0.0002697107,0.000182116,0.000170071,0.0008323361],"genre_scores_gemma":[0.09015793,0.00000131218,0.909126,0.0005518996,0.00003194548,0.00002287792,0.000004129702,0.00004043588,0.00006347296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2854104,"threshold_uncertainty_score":0.6734888,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4213270339","doi":"10.3390/stats5010013","title":"Bootstrap Prediction Intervals of Temporal Disaggregation","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Sieve (category theory); Econometrics; Interval (graph theory); Prediction interval; Series (stratigraphy); Quarter (Canadian coin); Computer science; TRACE (psycholinguistics); Time series; Statistics; Mathematics; Geography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.1566662343872108,"gpt":0.4080396346165272,"spread":0.2513734002293164,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003509144,0.00004854043,0.000109509,0.00003209771,0.00005541179,0.000005106838,0.00006969066,0.00001278515,0.0009208845],"category_scores_gemma":[0.000440459,0.00004485793,0.00003072535,0.0000894573,0.00003351607,0.00003301559,0.00005183729,0.0000847597,0.000002033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002629653,"about_ca_system_score_gemma":0.00002061149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002099048,"about_ca_topic_score_gemma":0.000002505075,"domain_scores_codex":[0.9992695,0.000129001,0.0002278879,0.00009322552,0.0001980916,0.00008221983],"domain_scores_gemma":[0.9994074,0.0003013886,0.000112381,0.0001186871,0.00003362425,0.00002654065],"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":[0.0001083549,0.0003662524,0.01243403,0.0002349683,0.00005193601,0.000006935283,0.002591715,0.00002279574,0.003180091,0.8389014,0.01241602,0.1296855],"study_design_scores_gemma":[0.0002402167,0.0003723451,0.007953282,0.00002630677,0.00001932109,0.000003715187,0.0005939023,0.002029596,0.001813106,0.9856753,0.001200567,0.00007229714],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5191616,0.00003763524,0.4719439,0.0001538831,0.0004890235,0.0002803757,0.000756564,0.0000748758,0.007102154],"genre_scores_gemma":[0.9306996,0.000001728265,0.06898521,0.00001896952,0.00002176629,0.00002620155,0.00001505189,0.000007311576,0.0002240966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4115381,"threshold_uncertainty_score":0.9999924,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2918504037","doi":"10.3390/stats2020014","title":"A Parametric Bayesian Approach in Density Ratio Estimation","year":2019,"lang":"en","type":"article","venue":"Stats","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University; Brock University","funders":"","keywords":"Frequentist inference; Estimator; Outlier; Bayesian probability; Divergence (linguistics); Parametric statistics; Bayes estimator; Mathematics; Statistics; Parametric model; Function (biology); Computer science; Econometrics; Bayesian inference","retraction":null,"screen_n_in":null,"score":{"opus":0.09449531020282669,"gpt":0.409953469547802,"spread":0.3154581593449753,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003450624,0.00008920324,0.0001986549,0.00009434467,0.00002266347,0.00001477277,0.00005918241,0.00004815052,0.00003708257],"category_scores_gemma":[0.0007013557,0.00007934252,0.00002399543,0.0002577768,0.00001735675,0.0001012712,0.00002289312,0.0001202576,0.00002850628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004640441,"about_ca_system_score_gemma":0.0000223694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001058508,"about_ca_topic_score_gemma":0.000005498953,"domain_scores_codex":[0.9991686,0.00009277678,0.0002038058,0.0002031851,0.0001510082,0.0001805892],"domain_scores_gemma":[0.9990292,0.0006244036,0.00006013192,0.0002042843,0.00003157771,0.00005038562],"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":[0.00005876749,0.0002407985,0.001420944,0.0002578721,0.00001180226,0.000009817178,0.0007475157,0.004084844,0.0002031087,0.9155298,0.0002402129,0.07719447],"study_design_scores_gemma":[0.0002552185,0.00002726452,0.001440043,0.00001120342,0.000005018979,0.000001931248,0.0000435872,0.3332059,0.0001223605,0.6647896,0.0000149594,0.00008293782],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08885357,0.00000948677,0.9068714,0.00001990728,0.00005087865,0.0003330209,0.000006491929,0.00003220864,0.003823078],"genre_scores_gemma":[0.4697517,9.404438e-7,0.530014,0.00002026479,0.000005399581,0.000009809977,0.000004139729,0.000007667497,0.0001859981],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3808982,"threshold_uncertainty_score":0.3235494,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4281981557","doi":"10.3390/stats5020031","title":"A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Winnipeg; University of Ottawa","funders":"National Institute on Minority Health and Health Disparities; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Computer science; Sampling (signal processing); Context (archaeology); Variance (accounting); Sampling design; Stability (learning theory); Algorithm; Stage (stratigraphy); Task (project management); Statistics; Data mining; Machine learning; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.924316499206533,"gpt":0.6509971269881859,"spread":0.2733193722183471,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01700583,0.00006083797,0.000185037,0.00006273763,0.0009685289,0.000009538634,0.000180622,0.00004084824,0.0002326591],"category_scores_gemma":[0.005299659,0.00006935895,0.00006388548,0.0001881583,0.0001537243,0.00004277734,0.00004651913,0.0001345929,0.000001302582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004987981,"about_ca_system_score_gemma":0.0001858126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006775334,"about_ca_topic_score_gemma":0.0005172815,"domain_scores_codex":[0.9944989,0.004649815,0.0002175664,0.0001680895,0.0001945896,0.0002710202],"domain_scores_gemma":[0.9902815,0.009373874,0.0001410751,0.00009162086,0.00005943185,0.00005254258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.01281511,0.001641543,0.1640926,0.0001812426,0.0002839362,0.00003231692,0.5470433,0.004916434,0.03794241,0.03946165,0.00214129,0.1894482],"study_design_scores_gemma":[0.004473011,0.001529492,0.1289135,0.00004608149,0.0001131111,0.000003252957,0.4144067,0.01740791,0.01603903,0.00629446,0.4096406,0.001132842],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4511281,0.0003738994,0.5454617,0.0001153618,0.0007940219,0.000521478,0.0002713964,0.00007059459,0.001263451],"genre_scores_gemma":[0.6882256,0.000005284525,0.3090697,0.00005557296,0.00007232736,0.00008638957,0.00001858684,0.00001253375,0.002454009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4074993,"threshold_uncertainty_score":0.7449239,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3143454015","doi":"10.3390/stats4020018","title":"Measuring Bayesian Robustness Using Rényi Divergence","year":2021,"lang":"en","type":"article","venue":"Stats","topic":"Advanced Statistical Methods and Models","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 Toronto","funders":"","keywords":"Prior probability; Robustness (evolution); Bayesian probability; Curvature; Computer science; Mathematics; Divergence (linguistics); Artificial intelligence; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.3417578879156906,"gpt":0.4389721859944113,"spread":0.09721429807872078,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002053721,0.000111735,0.0001969069,0.00002174627,0.0001266366,0.00002197011,0.00008304035,0.00003979178,0.0001951774],"category_scores_gemma":[0.0009241826,0.0001075821,0.00004566537,0.0001361966,0.0000351551,0.00009727722,0.00008993281,0.0001108524,0.000003750025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000435351,"about_ca_system_score_gemma":0.00006333702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006449862,"about_ca_topic_score_gemma":0.00001511646,"domain_scores_codex":[0.9989592,0.0001048108,0.0002005735,0.0002496793,0.0002167589,0.000268933],"domain_scores_gemma":[0.9991116,0.0003427446,0.00005585068,0.0002454628,0.0001381461,0.0001061862],"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":[0.00005292013,0.0002814335,0.000457736,0.0006270429,0.0001102032,0.0008838036,0.001339913,0.01571869,0.02309452,0.8685563,0.0004434584,0.08843399],"study_design_scores_gemma":[0.0002353607,0.00001312304,0.0001012825,0.0001089066,0.00004464818,0.000028683,0.0003057899,0.06155023,0.01083261,0.9263021,0.0002175946,0.0002596924],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02630495,0.00009002101,0.9714801,0.00003647549,0.0002276473,0.00006811612,0.00002609028,0.00004876982,0.001717822],"genre_scores_gemma":[0.1618384,0.00001281045,0.8377103,0.00003030727,0.00005207185,0.000003646037,0.000001712753,0.00002149705,0.000329297],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1355334,"threshold_uncertainty_score":0.4387071,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386256573","doi":"10.3390/stats6030056","title":"Investigating Self-Rationalizing Models for Commonsense Reasoning","year":2023,"lang":"en","type":"article","venue":"Stats","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Computer science; Generative grammar; Leverage (statistics); Commonsense reasoning; Language model; Transformer; Artificial intelligence; Natural language processing; Natural language understanding; Generative model; Natural language; Representation (politics)","retraction":null,"screen_n_in":null,"score":{"opus":0.09268685221712009,"gpt":0.3107939534302409,"spread":0.2181071012131208,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004544136,0.00007732608,0.00008723541,0.00007412752,0.0002251374,0.0001144682,0.0002324456,0.00002968803,7.970669e-7],"category_scores_gemma":[0.0001215614,0.00008225307,0.00003402931,0.0002849319,0.00001155142,0.0003922604,0.0001518409,0.00006695999,0.0000174407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003136059,"about_ca_system_score_gemma":0.00009125085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002468743,"about_ca_topic_score_gemma":0.00001002869,"domain_scores_codex":[0.9990878,0.00003687046,0.0001714678,0.0002625873,0.000185246,0.0002560453],"domain_scores_gemma":[0.9992555,0.000257924,0.00005730741,0.0002897593,0.00007019073,0.00006926439],"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":[0.000001016511,0.000009723216,0.0003199418,0.00004659605,0.00001879384,0.000009741979,0.00856918,0.1335933,0.0007554715,0.84258,0.004362532,0.009733771],"study_design_scores_gemma":[0.0001184658,0.000008833933,0.00007266493,0.00002682011,0.00000187088,0.000003599234,0.00006902479,0.8603777,0.0002015575,0.1384586,0.0005765994,0.00008429791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06891495,0.00002944089,0.927686,0.001257173,0.0002026444,0.0001614523,0.000004301157,0.000737198,0.0010068],"genre_scores_gemma":[0.4431973,0.000002920438,0.5561799,0.000367753,0.00007166979,0.00003470184,0.000008615038,0.00001057327,0.0001265823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7267844,"threshold_uncertainty_score":0.3354183,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4283804517","doi":"10.3390/stats5030036","title":"Quantile Regression Approach for Analyzing Similarity of Gene Expressions under Multiple Biological Conditions","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Mahalanobis distance; Quantile regression; Similarity (geometry); Statistic; Expression (computer science); Computer science; Cluster analysis; Statistics; Statistical hypothesis testing; Test statistic; Function (biology); Mathematics; Biology; Genetics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.07084425239305296,"gpt":0.3346029531168182,"spread":0.2637587007237652,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001588848,0.00009529757,0.0001230986,0.00005207225,0.0003162332,0.000006194504,0.000165268,0.00007261509,0.00007829215],"category_scores_gemma":[0.00006498926,0.00007967759,0.0001049282,0.0001115073,0.00006130217,0.000002888867,0.0001242417,0.00008122678,3.388438e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001585404,"about_ca_system_score_gemma":0.00006240982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005337166,"about_ca_topic_score_gemma":0.000001491531,"domain_scores_codex":[0.999109,0.0001125493,0.0001917757,0.0003181243,0.0001133367,0.0001552344],"domain_scores_gemma":[0.9994205,0.00002800321,0.0001342456,0.0002922196,0.00006762073,0.0000573922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001045139,0.0001777207,0.002129302,0.00001002036,0.00001879145,1.493648e-7,0.0000465655,0.002269879,0.9871079,0.0002201091,0.007665073,0.0002499899],"study_design_scores_gemma":[0.001342824,0.000396676,0.01175953,0.00001004279,0.0000240592,0.000004979241,0.002007619,0.002584484,0.9523757,0.000641337,0.02858882,0.0002639634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8245186,0.000597764,0.1729186,0.0001300745,0.0001668248,0.0004385743,0.0008412556,0.0000202903,0.0003679776],"genre_scores_gemma":[0.9898762,0.00004914347,0.007225073,0.0001053812,0.00005385057,0.0003159068,0.002118722,0.00001212434,0.0002436261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1656935,"threshold_uncertainty_score":0.3249158,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386849819","doi":"10.3390/stats6030059","title":"A Family of Finite Mixture Distributions for Modelling Dispersion in Count Data","year":2023,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian 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 Victoria","funders":"Ministry of Higher Education; UCSI University","keywords":"Negative binomial distribution; Count data; Poisson distribution; Dispersion (optics); Mathematics; Mixture model; Applied mathematics; Binomial distribution; Overdispersion; Mixture distribution; Index of dispersion; Compound Poisson distribution; Binomial (polynomial); Statistical physics; Flexibility (engineering); Exponential family; Likelihood function; Probability distribution; Statistics; Probability density function; Estimation theory; Poisson regression; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.2629780494088379,"gpt":0.4333171336539085,"spread":0.1703390842450706,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004112613,0.00006543015,0.0001597944,0.00004616208,0.00003734309,0.000009785404,0.0001726263,0.000041306,0.00001206685],"category_scores_gemma":[0.001212595,0.00005522733,0.00002245476,0.0002429173,0.00003416785,0.0000500367,0.00008243395,0.00006946881,0.000004931278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001595148,"about_ca_system_score_gemma":0.00003591628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003395153,"about_ca_topic_score_gemma":0.00001727871,"domain_scores_codex":[0.999299,0.00003419085,0.0002153851,0.0001681718,0.0001165184,0.000166684],"domain_scores_gemma":[0.9974352,0.002114648,0.00005103934,0.0003132682,0.00005387774,0.00003199985],"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":[0.0000640398,0.0001234718,0.0002866434,0.0004903037,0.00001958551,0.00000943653,0.0005079741,0.0002360971,0.0007794002,0.9646655,0.01160323,0.02121427],"study_design_scores_gemma":[0.0001672919,0.00002306933,0.0002956609,0.0000831552,0.00001258556,1.380752e-7,0.00009905853,0.34096,0.00006495413,0.6575583,0.0006799529,0.00005576565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009019738,0.00002393349,0.983403,0.000104057,0.00006504418,0.0001934825,0.007033892,0.00002550818,0.000131336],"genre_scores_gemma":[0.1557869,0.0000616426,0.8434443,0.00001552739,0.00002439205,0.00002257616,0.0005848677,0.00001231764,0.00004755534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3407239,"threshold_uncertainty_score":0.2252105,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3154533874","doi":"10.3390/stats4020021","title":"A Flexible Multivariate Distribution for Correlated Count Data","year":2021,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian 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":"McMaster University","funders":"","keywords":"Count data; Multivariate statistics; Negative binomial distribution; Poisson distribution; Statistics; Dispersion (optics); Mathematics; Multivariate analysis of variance; Multivariate normal distribution; Multivariate analysis; Overdispersion; Computer science; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.2343690777199644,"gpt":0.4616250624967598,"spread":0.2272559847767954,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003574863,0.00009043491,0.0001751109,0.000009176882,0.00008530714,0.00004512461,0.0001543587,0.00005725155,0.0002277464],"category_scores_gemma":[0.004774261,0.00007975355,0.00002532059,0.0001241914,0.00003178175,0.00007540151,0.0001135471,0.00008691027,0.00002121719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003362022,"about_ca_system_score_gemma":0.0001120699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000196178,"about_ca_topic_score_gemma":0.00001087333,"domain_scores_codex":[0.9990981,0.0000743292,0.0002157563,0.0002752933,0.0001267927,0.0002097363],"domain_scores_gemma":[0.9976819,0.001497615,0.00006759084,0.0004985904,0.000190493,0.00006377377],"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":[0.00004555922,0.0001212035,0.0000457032,0.000111965,0.00003860617,0.0000194363,0.00008531531,3.701938e-7,0.0007478786,0.9253845,0.02570235,0.04769713],"study_design_scores_gemma":[0.0005927207,0.00003976984,0.0006191471,0.00006200808,0.00006325928,0.000007628441,0.00005793924,0.01870538,0.00213983,0.9616371,0.01593533,0.0001398632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004673462,0.00003684067,0.9919847,0.0001834389,0.0002781255,0.0001923312,0.006131663,0.00006802149,0.000657585],"genre_scores_gemma":[0.02042783,0.0000107049,0.9768691,0.00006685779,0.00006221495,0.00002499424,0.001947181,0.00001724372,0.0005738747],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04755726,"threshold_uncertainty_score":0.5715581,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4212841968","doi":"10.3390/stats5010012","title":"Multivariate Threshold Regression Models with Cure Rates: Identification and Estimation in the Presence of the Esscher Property","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Distribution Estimation and Applications","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":"McGill University; Ottawa Hospital","funders":"National Institutes of Health","keywords":"Econometrics; Multivariate statistics; Univariate; Censoring (clinical trials); Mathematics; Regression; Regression analysis; Stochastic process; Statistics; Applied mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.09534211727502717,"gpt":0.3716421310235123,"spread":0.2763000137484852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003959949,0.00006868581,0.00007869534,0.00002203319,0.0002414643,0.00002560313,0.0001785705,0.00001713663,0.00003815507],"category_scores_gemma":[0.000193715,0.00003025713,0.00001346129,0.0002957963,0.0000920563,0.0001131786,0.00006108123,0.0001417091,0.00000100652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002502246,"about_ca_system_score_gemma":0.00003656494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003196359,"about_ca_topic_score_gemma":0.00001184316,"domain_scores_codex":[0.9991001,0.0001515358,0.0002179126,0.0001404485,0.0003081865,0.0000817421],"domain_scores_gemma":[0.9992275,0.0002480425,0.0001635616,0.0002765044,0.000068364,0.0000160672],"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":[0.00004537605,0.000248864,0.000362801,0.00007615214,0.000007918818,6.742154e-7,0.003978144,0.009647459,0.001029099,0.9798797,0.002908663,0.00181512],"study_design_scores_gemma":[0.0003440365,0.00002177714,0.01241804,0.00005187622,0.00001915424,0.000004685303,0.001070556,0.6475064,0.0004623478,0.3379227,0.0001091044,0.00006929835],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1625516,0.00004414183,0.8295591,0.004674898,0.00004368706,0.001703967,0.0003208089,0.00003804606,0.001063738],"genre_scores_gemma":[0.9949471,0.000003175522,0.00439216,0.00004860746,0.000002658072,0.0003183937,0.00003222712,0.000006387994,0.0002492833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8323955,"threshold_uncertainty_score":0.1857173,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4210304595","doi":"10.3390/stats5010009","title":"Selection of Auxiliary Variables for Three-Fold Linking Models in Small Area Estimation: A Simple and Effective Method","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","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","keywords":"Covariate; Statistics; Feature selection; Selection (genetic algorithm); Regression analysis; Model selection; Sample size determination; Variable (mathematics); Mathematics; Computer science; Econometrics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.1434506400135253,"gpt":0.388089994620137,"spread":0.2446393546066117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004273806,0.00008830874,0.0002231682,0.000347067,0.0002114862,0.00004889683,0.0001391893,0.00003806287,0.00002897541],"category_scores_gemma":[0.0004181883,0.00008018051,0.00005404098,0.0007799202,0.00002119711,0.0002027328,0.00007651447,0.0001099098,3.091762e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003522379,"about_ca_system_score_gemma":0.00005462717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002233763,"about_ca_topic_score_gemma":0.0005236272,"domain_scores_codex":[0.9984599,0.0002445534,0.0004163941,0.0003463313,0.0003769831,0.0001558899],"domain_scores_gemma":[0.9971588,0.002280994,0.0002047024,0.0001428599,0.0001803306,0.00003236641],"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":[0.0001399636,0.00004904647,0.003744398,0.00002428879,0.00001480844,6.53814e-7,0.001785509,0.7745566,0.0004703986,0.008216602,0.00005287642,0.2109448],"study_design_scores_gemma":[0.0002650702,0.0001003364,0.001283034,0.000008153649,0.00000935946,0.000002241394,0.0003631682,0.5632752,0.0000616506,0.4345383,0.00004660104,0.00004691862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3175869,0.00005505659,0.6816563,0.0000489695,0.00006249885,0.0003580713,0.00004096532,0.00001707631,0.0001741582],"genre_scores_gemma":[0.9170474,0.000004647736,0.08264213,0.00003626736,0.000009326954,0.0002060084,0.00002287537,0.000008477392,0.00002285258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5994605,"threshold_uncertainty_score":0.3269666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3204285967","doi":"10.3390/stats6030052","title":"Analysis of Ordinal Populations from Judgment Post-Stratification","year":2023,"lang":"en","type":"article","venue":"Stats","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Ordinal data; Estimator; Statistics; Ranking (information retrieval); Econometrics; Computer science; Population; Data collection; Estimation; Stratified sampling; Stratification (seeds); Data mining; Mathematics; Artificial intelligence; Medicine; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2659439969973931,"gpt":0.452586694598778,"spread":0.186642697601385,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000186977,0.00005464622,0.0001736091,0.0001445116,0.00003689209,0.00001097455,0.0000652447,0.00002596803,0.0006106927],"category_scores_gemma":[0.0007708235,0.00004781066,0.00005771329,0.0007303936,0.00002468537,0.00002547031,0.00001751301,0.00003920523,0.00003077153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001274518,"about_ca_system_score_gemma":0.00001820385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003005789,"about_ca_topic_score_gemma":0.0001598311,"domain_scores_codex":[0.999293,0.00006002477,0.0002585705,0.0001257367,0.0001681183,0.00009449416],"domain_scores_gemma":[0.9989199,0.0006323257,0.0000935471,0.0002112547,0.0001053924,0.00003757555],"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":[0.00001809861,0.0001047824,0.01352844,0.00002881597,0.0004249281,0.000002994223,0.001060755,0.00009371815,0.009010086,0.9393929,0.001686694,0.03464775],"study_design_scores_gemma":[0.00006301136,0.00003161135,0.3862797,0.000008569102,0.0004245109,4.177011e-8,0.0002382134,0.01365546,0.0005877322,0.5986081,0.00003979564,0.00006323955],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.703768,0.000006417763,0.2931934,0.0002868393,0.00009372036,0.0001030215,0.001023925,0.00006020266,0.001464554],"genre_scores_gemma":[0.8225533,0.000002355596,0.1770164,0.00001516989,0.00001329711,0.000009753814,0.0002403622,0.000005157035,0.0001441484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3727512,"threshold_uncertainty_score":0.6686657,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2955134940","doi":"10.3390/stats2030024","title":"Confidence Sets for Statistical Classification","year":2019,"lang":"en","type":"article","venue":"Stats","topic":"Advanced Statistical Methods and Models","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":"Acadia University","funders":"","keywords":"Estimator; Classifier (UML); Computer science; Artificial intelligence; Statistical inference; Inference; Classification rule; Machine learning; Pattern recognition (psychology); Data mining; Object (grammar); Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.272529505767471,"gpt":0.5125858415768718,"spread":0.2400563358094008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002868368,0.00009262803,0.0001802096,0.00002073587,0.00004206442,0.00001569003,0.00008201902,0.0000453624,0.0002186903],"category_scores_gemma":[0.001119303,0.00008080434,0.00002763003,0.00003849661,0.00004896831,0.00006913222,0.00001655194,0.00007373229,0.00007896712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002748827,"about_ca_system_score_gemma":0.00003483814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002142038,"about_ca_topic_score_gemma":0.000002527821,"domain_scores_codex":[0.9991304,0.00004958475,0.0002130717,0.0002398515,0.0001472897,0.000219772],"domain_scores_gemma":[0.9968954,0.002632946,0.00006997228,0.0002253875,0.00009763772,0.0000786125],"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":[0.00004576558,0.00002680893,0.00001515323,0.0001015648,0.00000649672,9.250008e-7,0.00008250562,0.00000276302,0.001293022,0.9784142,0.00200458,0.01800625],"study_design_scores_gemma":[0.0003486288,0.0000988264,0.000311562,0.00001898604,0.00001420963,0.000001536708,0.00009060613,0.01205086,0.0003618135,0.9826199,0.003969604,0.0001134552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003667864,0.00001012594,0.9917427,0.0001270432,0.000179179,0.00056183,0.0003428562,0.00005052699,0.003317853],"genre_scores_gemma":[0.188583,0.000004060638,0.8102093,0.0000884291,0.00002233061,0.00005578359,0.00002409195,0.00001884497,0.0009941657],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1849152,"threshold_uncertainty_score":0.3295105,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2940139440","doi":"10.3390/stats2020017","title":"A Bayesian Approach to Predict the Number of Goals in Hockey","year":2019,"lang":"en","type":"article","venue":"Stats","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Bayesian probability; Outcome (game theory); Computer science; Bayesian game; Machine learning; Artificial intelligence; Game theory; Sequential game; Mathematics; Mathematical economics","retraction":null,"screen_n_in":null,"score":{"opus":0.02024603813308638,"gpt":0.2294461440738692,"spread":0.2092001059407828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003521861,0.00006671744,0.0002079011,0.00007623996,0.00001652602,0.00001674364,0.0001682259,0.00003291606,0.0005643912],"category_scores_gemma":[0.00001197974,0.00005496129,0.00004471484,0.0002341372,0.0000169567,0.00006290078,0.0000348834,0.00007347299,0.0004379954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002208527,"about_ca_system_score_gemma":0.00001221479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004150987,"about_ca_topic_score_gemma":0.00002589605,"domain_scores_codex":[0.9993001,0.00000337053,0.0003217619,0.000178644,0.00003111507,0.0001650521],"domain_scores_gemma":[0.9995385,0.00001484576,0.0001134658,0.0002855949,0.00001213602,0.00003549517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000943456,0.00004733294,0.9348728,0.00002245009,0.00001182582,3.476412e-7,0.0008398908,0.001422293,0.000001595925,0.06141217,0.001053098,0.0003067703],"study_design_scores_gemma":[0.0005476514,0.00005119185,0.8734659,0.00002691732,0.000003139534,0.000002376427,0.0002827221,0.04960785,0.00002920758,0.01326027,0.06249099,0.0002318066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8172521,0.0001203904,0.001018864,0.0001617532,0.0001379577,0.0002030591,0.00007664369,0.000004974519,0.1810243],"genre_scores_gemma":[0.9968417,0.00004746436,0.0002980524,0.0001877377,0.00002988957,0.00001068873,0.000006218677,0.000009876589,0.002568341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1795897,"threshold_uncertainty_score":0.6179687,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4394995686","doi":"10.3390/stats7020023","title":"New Goodness-of-Fit Tests for the Kumaraswamy Distribution","year":2024,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Distribution Estimation and Applications","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 Victoria","funders":"","keywords":"Goodness of fit; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.2092862122156154,"gpt":0.4616240666068015,"spread":0.2523378543911862,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001469459,0.00007730325,0.00009471295,0.00001478145,0.00009722291,0.00004609359,0.000109876,0.00003387459,0.0002849122],"category_scores_gemma":[0.0007178285,0.00005340246,0.00006470008,0.0002223383,0.00005031096,0.00004924608,0.00001734957,0.00006168768,0.00006683265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003644535,"about_ca_system_score_gemma":0.00009284927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001216119,"about_ca_topic_score_gemma":0.00000650185,"domain_scores_codex":[0.9993699,0.00001154251,0.0002237993,0.0001304241,0.0001365652,0.0001277804],"domain_scores_gemma":[0.9977364,0.001884882,0.00004512862,0.0001883809,0.00008698508,0.00005824468],"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":[0.000004061239,0.00002128029,0.000006008131,0.0000774022,0.00001665372,2.263604e-7,0.00005094659,0.000005165712,0.00007723932,0.8271075,0.146892,0.02574145],"study_design_scores_gemma":[0.0002052843,0.00003049639,0.00269239,0.00006031187,0.00009744953,0.000003070708,0.00007312791,0.01777005,0.0007811409,0.8721706,0.1060126,0.0001034498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005915257,0.0001846562,0.9934809,0.002022072,0.0001641913,0.0004062165,0.002424855,0.0001118896,0.0006136987],"genre_scores_gemma":[0.974576,0.00001732016,0.02245609,0.00004732884,0.0001282481,0.000179151,0.0004774867,0.00001889214,0.002099481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9739845,"threshold_uncertainty_score":0.3119588,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414594302","doi":"10.3390/stats8030081","title":"The Unit-Modified Weibull Distribution: Theory, Estimation, and Real-World Applications","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Distribution Estimation and Applications","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":"Dalhousie University","funders":"King Saud University","keywords":"Nonparametric statistics; Weibull distribution; Estimator; Parametric statistics; Confidence interval; Parametric model; Statistical hypothesis testing; Probability distribution","retraction":null,"screen_n_in":null,"score":{"opus":0.04374741793604851,"gpt":0.3949918364482185,"spread":0.35124441851217,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004236419,0.0001341311,0.0001368067,0.00004356637,0.0009557139,0.000125081,0.0001849626,0.00004491419,0.00006090305],"category_scores_gemma":[0.0006459871,0.0001033463,0.00003429365,0.0006005049,0.0003168093,0.000059044,0.00006283494,0.000124901,0.00004986675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006264911,"about_ca_system_score_gemma":0.00008696361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001744426,"about_ca_topic_score_gemma":0.00006662186,"domain_scores_codex":[0.9989386,0.00009643993,0.0003692426,0.0002262645,0.0001582446,0.000211233],"domain_scores_gemma":[0.9968236,0.002339617,0.0001056921,0.0004310427,0.0002108727,0.00008917797],"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":[0.00001108135,0.00004633581,0.00005568259,0.00002769598,0.00002033726,1.753682e-7,0.00002339641,0.000007903409,0.000006884703,0.9541117,0.01725729,0.02843152],"study_design_scores_gemma":[0.000242582,0.00000592703,0.008479822,0.0000181111,0.00005214369,0.000001050806,0.00009258094,0.006348116,0.00007238822,0.9143916,0.07019618,0.00009954023],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005953556,0.00006160622,0.962445,0.004088376,0.00005062703,0.000643078,0.0006631365,0.0001797642,0.03127307],"genre_scores_gemma":[0.9723343,0.0001341345,0.01099418,0.0002768833,0.00004569343,0.001246072,0.0009232599,0.00001848565,0.01402704],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9717389,"threshold_uncertainty_score":0.7350675,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406149795","doi":"10.3390/stats8010005","title":"Exact Inference for Random Effects Meta-Analyses for Small, Sparse Data","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Meta-analysis and systematic reviews","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":"University of Toronto","funders":"National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; University of Toronto","keywords":"Inference; Statistical inference; Sample size determination; Computer science; Meta-analysis; Event (particle physics); Contrast (vision); Data mining; Causal inference; Frequentist inference; Rare events; Econometrics; Statistics; Mathematics; Artificial intelligence; Bayesian inference; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.9433484354650475,"gpt":0.650374056839172,"spread":0.2929743786258755,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.08489258,0.0003522052,0.005619329,0.0003976461,0.0002090651,0.0009875799,0.003641792,0.00007413424,0.001712629],"category_scores_gemma":[0.1744164,0.000150768,0.003166836,0.001077585,0.00005133556,0.0003229401,0.0003634191,0.00007955829,0.0003510619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001575472,"about_ca_system_score_gemma":0.0001677495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002209423,"about_ca_topic_score_gemma":0.0001805658,"domain_scores_codex":[0.9866387,0.00422347,0.005548193,0.001463263,0.001773478,0.0003529097],"domain_scores_gemma":[0.9310586,0.0569741,0.002659331,0.007876551,0.00129387,0.0001375037],"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":[0.0001985921,0.0001427564,0.0009614331,0.0008268867,0.02067211,0.000003816987,0.0002078166,0.0003024759,0.0005922756,0.01053457,0.8549738,0.1105834],"study_design_scores_gemma":[0.002464996,0.0001055601,0.0008687993,0.00005327628,0.03667964,9.304775e-7,0.0001972057,0.04895459,0.001084465,0.09000324,0.8192101,0.0003772187],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00305134,0.004503084,0.9840845,0.0008825994,0.0004738461,0.003542504,0.000575696,0.000009954862,0.002876509],"genre_scores_gemma":[0.6949537,0.0001181161,0.1888145,0.00243079,0.0002192321,0.002458814,0.0005380734,0.00004333169,0.1104234],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.79527,"threshold_uncertainty_score":0.9991999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3028982422","doi":"10.3390/stats3020011","title":"A-Spline Regression for Fitting a Nonparametric Regression Function with Censored Data","year":2020,"lang":"en","type":"article","venue":"Stats","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":"Regional Municipality of Niagara; Brock University","funders":"","keywords":"Smoothing spline; Nonparametric regression; Spline (mechanical); Estimator; Nonparametric statistics; Mathematics; Smoothing; Kernel regression; Kernel smoother; Censored regression model; Regression analysis; Polynomial regression; Censoring (clinical trials); Computer science; Kernel method; Statistics; Artificial intelligence; Spline interpolation; Support vector machine; Engineering; Radial basis function kernel","retraction":null,"screen_n_in":null,"score":{"opus":0.2806591337634829,"gpt":0.433931932130236,"spread":0.1532727983667531,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003829025,0.0001566507,0.0002699425,0.000046786,0.0001231752,0.00003717105,0.0002315329,0.00006466258,0.00009157535],"category_scores_gemma":[0.006821388,0.00009554537,0.00002596391,0.000361184,0.00003802264,0.0001079835,0.0001371746,0.0001561008,0.00001035165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001481222,"about_ca_system_score_gemma":0.00003982986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004672595,"about_ca_topic_score_gemma":0.00000198816,"domain_scores_codex":[0.9987191,0.00008343619,0.0002788672,0.0004296997,0.0002535018,0.0002353952],"domain_scores_gemma":[0.9971636,0.001888424,0.0001978032,0.000475134,0.0001326874,0.0001423364],"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":[0.003034895,0.0002559362,0.0009058342,0.001294422,0.0001021586,0.00002883083,0.0009825791,0.00001244919,0.004337553,0.08341415,0.07992555,0.8257056],"study_design_scores_gemma":[0.007766679,0.005452435,0.004018264,0.002610319,0.0006851418,0.00002121842,0.001286856,0.4592585,0.009153689,0.4650944,0.043134,0.001518501],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01786305,0.00007023272,0.9797171,0.0007583238,0.000123629,0.0004370903,0.0002938083,0.0001135825,0.0006231287],"genre_scores_gemma":[0.09154338,0.00001240015,0.9076728,0.0002489056,0.0001874305,0.0000220275,0.0001223168,0.00003285061,0.0001578527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8241872,"threshold_uncertainty_score":0.8166331,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413773115","doi":"10.3390/stats8030077","title":"A Markov Chain Monte Carlo Procedure for Efficient Bayesian Inference on the Phase-Type Aging Model","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Monte Carlo method; Inference; Bayesian inference; Markov chain; Hybrid Monte Carlo; Computer science; Bayesian probability; Statistical physics; Mathematics; Artificial intelligence; Statistics; Machine learning; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03146989526737381,"gpt":0.36860158399594,"spread":0.3371316887285661,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095017,0.0001550767,0.0001588639,0.0001410767,0.0008130306,0.0001239368,0.0004175378,0.00005744658,0.00001456351],"category_scores_gemma":[0.0003070327,0.0001209497,0.00009913315,0.0006448285,0.0001930442,0.00005876152,0.00006177128,0.0001453528,0.000004678256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001033039,"about_ca_system_score_gemma":0.0002601602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005837785,"about_ca_topic_score_gemma":0.002303831,"domain_scores_codex":[0.9984295,0.0001252618,0.0002073234,0.0003474554,0.000417318,0.0004730911],"domain_scores_gemma":[0.9991761,0.0002029371,0.00008646962,0.0003082601,0.0001636694,0.00006250402],"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":[0.0003540691,0.0008484298,0.008507223,0.0003716829,0.0002597475,0.00001092217,0.0476588,0.05674164,0.00009543209,0.7991542,0.03428879,0.05170905],"study_design_scores_gemma":[0.002264589,0.0002727741,0.006370908,0.0004794431,0.000158553,1.356618e-7,0.01635468,0.8915818,0.0002122706,0.0493025,0.03221072,0.0007916096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8601768,0.0003939256,0.03699718,0.01203778,0.001244547,0.004542991,0.0001262808,0.0003041013,0.08417636],"genre_scores_gemma":[0.9949452,0.00006073307,0.0004223931,0.001133627,0.00006609486,0.0001901246,0.000003291211,0.00001243538,0.003166082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8348402,"threshold_uncertainty_score":0.6253256,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403620584","doi":"10.3390/stats7040074","title":"Statistical Distribution Theory and Fractional Calculus","year":2024,"lang":"en","type":"article","venue":"Stats","topic":"Fractional Differential Equations Solutions","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":"McGill University","funders":"","keywords":"Calculus (dental); Fractional calculus; Distribution (mathematics); Mathematics; Applied mathematics; Mathematical analysis; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.04368095342542489,"gpt":0.3728450999373811,"spread":0.3291641465119562,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002486436,0.00007625879,0.00007349819,0.00004311714,0.0001345733,0.00006710878,0.00002841398,0.00004413147,0.0008282543],"category_scores_gemma":[0.0007217028,0.00006893705,0.00002729947,0.0001019868,0.00009027982,0.0001399409,0.0000233416,0.0001501752,0.0001629959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009516357,"about_ca_system_score_gemma":0.00006887643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001525118,"about_ca_topic_score_gemma":0.00001453983,"domain_scores_codex":[0.9992891,0.00009071008,0.0001402723,0.0001611442,0.0001923472,0.0001264442],"domain_scores_gemma":[0.9980267,0.001764413,0.00001747678,0.00008348717,0.00004564147,0.00006234012],"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":[0.00001386845,0.00004013696,0.00001459846,0.00003004686,0.00003353694,0.000006997853,0.00005873875,0.000003277515,0.00009784925,0.9790563,0.01539732,0.005247327],"study_design_scores_gemma":[0.00008058717,0.00001974014,0.005658215,0.00002516021,0.00005206608,0.00002473595,0.00006272649,0.005826177,0.00003674514,0.9742823,0.01384453,0.00008702138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02043946,0.0001780295,0.9755874,0.000453806,0.000400652,0.0001004834,0.001051805,0.0001532551,0.00163512],"genre_scores_gemma":[0.9951062,0.00001559029,0.002983611,0.00002309782,0.0001249272,0.00002621317,0.0003621193,0.00001369491,0.001344613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9746667,"threshold_uncertainty_score":0.9068804,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405073591","doi":"10.3390/stats7040084","title":"Child Labor in Sindh, Pakistan: Patterns and Areas in Need of Intervention","year":2024,"lang":"en","type":"article","venue":"Stats","topic":"Poverty, Education, and Child Welfare","field":"Social Sciences","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 Saskatchewan","funders":"UNICEF","keywords":"Socioeconomic status; Poverty; Geospatial analysis; Intervention (counseling); Geography; Socioeconomics; Developing country; Child labour; Environmental health; Demography; Medicine; Economic growth; Population; Economics; Sociology; Cartography; Work (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01113153202196338,"gpt":0.3184782303112151,"spread":0.3073466982892517,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002453509,0.0000411617,0.0000681052,0.00009149145,0.00004150705,0.00003564038,0.00005439632,0.00003290197,0.0001004112],"category_scores_gemma":[0.00003051882,0.00004039381,0.00001775195,0.000283346,0.00004240062,0.0001309781,0.000008441412,0.00006471773,0.000002648525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005247999,"about_ca_system_score_gemma":0.00007214922,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01768439,"about_ca_topic_score_gemma":0.09474088,"domain_scores_codex":[0.9994747,0.00006998816,0.0001358812,0.0001081201,0.0001013682,0.000109941],"domain_scores_gemma":[0.9998408,0.00003697093,0.000022173,0.0000496079,0.00002048621,0.000029992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001475924,0.0001633779,0.8258485,0.0001639876,0.00001044084,0.000008434375,0.09531595,0.000001671137,0.00001112759,0.02206084,0.0004765677,0.05592434],"study_design_scores_gemma":[0.0001789777,0.0000199792,0.9624652,0.0003546352,0.000003268849,3.257114e-7,0.01671621,0.00001661859,0.0000329601,0.004219571,0.01593224,0.00006003908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879633,0.001896681,0.00001186701,0.00212492,0.0002726596,0.00009281455,0.00005165038,0.00001447773,0.007571602],"genre_scores_gemma":[0.999149,0.0003944272,0.00001035265,0.00004502637,0.0000569763,0.000003866765,0.00001089286,0.000004052622,0.0003254329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1366167,"threshold_uncertainty_score":0.9888569,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4310525280","doi":"10.3390/stats5040075","title":"A Bayesian One-Sample Test for Proportion","year":2022,"lang":"en","type":"article","venue":"Stats","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":"Canada Research Chairs; McMaster University; University of Toronto","funders":"","keywords":"Mathematics; Statistics; A priori and a posteriori; Divergence (linguistics); Bayesian probability; Null hypothesis; Bernoulli's principle; Sample size determination; Kullback–Leibler divergence; Null (SQL); Binomial distribution; Measure (data warehouse); Binomial (polynomial); Negative binomial distribution; Sample (material); Computer science; Poisson distribution; Physics; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.1142653444930024,"gpt":0.3971139948043215,"spread":0.2828486503113191,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004346139,0.00007974567,0.0001507313,0.00003768228,0.0002371419,0.0000207679,0.0001045512,0.00001853001,0.001114898],"category_scores_gemma":[0.003811523,0.00007703579,0.00004472405,0.0001179745,0.00002870902,0.00003194368,0.00005671695,0.0001022191,0.000003935199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005118247,"about_ca_system_score_gemma":0.00005962257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002237607,"about_ca_topic_score_gemma":0.00001065697,"domain_scores_codex":[0.9991022,0.00006133469,0.0002180865,0.0001850599,0.0002179404,0.0002153977],"domain_scores_gemma":[0.9969925,0.002624595,0.00009186341,0.0001766423,0.00005474628,0.0000596535],"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":[0.00003715452,0.0003080128,0.0006163394,0.0001310268,0.00001371639,0.000003686747,0.0003321467,7.571685e-7,0.0006131507,0.9028009,0.004737348,0.09040581],"study_design_scores_gemma":[0.0002384523,0.0003357051,0.0003174413,0.000007756486,0.0000221899,0.00000290966,0.0001360278,0.001582925,0.0003209753,0.990144,0.006777429,0.0001141609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006891981,0.000006614211,0.9962543,0.0003174062,0.0001181143,0.0004814255,0.0007992797,0.00006805779,0.001265623],"genre_scores_gemma":[0.08526986,8.946512e-7,0.9138944,0.0001067722,0.00005759342,0.0003752679,0.00002927793,0.00002033424,0.0002456142],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09029166,"threshold_uncertainty_score":0.9997982,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4309787599","doi":"10.3390/stats5040071","title":"Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"King Abdullah University of Science and Technology; Qatar Foundation","keywords":"Representation (politics); Computer science; Degrees of freedom (physics and chemistry); Bayesian probability; Oracle; Standard deviation; Term (time); Linear model; Process (computing); Algorithm; Mathematics; Statistics; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.2811533763356427,"gpt":0.3652654018175243,"spread":0.08411202548188157,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001779988,0.00007905126,0.0002170566,0.0001900756,0.0001096667,0.0000180362,0.0003362612,0.00001706935,0.0001171819],"category_scores_gemma":[0.0007984289,0.00006408001,0.00005760595,0.0005595486,0.0000507897,0.00008296924,0.0002079276,0.00007359547,0.00000405946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004647901,"about_ca_system_score_gemma":0.0001069522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009758421,"about_ca_topic_score_gemma":0.00001690978,"domain_scores_codex":[0.9980288,0.0001522277,0.0004847256,0.0002418895,0.0009686607,0.0001237606],"domain_scores_gemma":[0.9986473,0.0004522702,0.0001915288,0.000476136,0.0001837313,0.00004902413],"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":[0.00002670132,0.0003577251,0.00115292,0.000005579282,0.00001406012,0.0001069783,0.004136289,0.9890021,0.001704049,0.0005770446,0.001085718,0.001830812],"study_design_scores_gemma":[0.001081418,0.001227662,0.0002287409,0.000007512498,0.00005204361,0.0002642715,0.01833552,0.9600682,0.0008342052,0.01736874,0.0002892876,0.0002424331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8362563,0.00004370979,0.1626396,0.00001331993,0.0001944573,0.0002407452,0.0000650245,0.00002026321,0.0005265238],"genre_scores_gemma":[0.990625,2.510376e-7,0.009047933,0.000003686275,0.00001061104,0.00001711748,0.000001294601,0.000008087051,0.0002860307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1543687,"threshold_uncertainty_score":0.2613107,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413128106","doi":"10.3390/stats8030073","title":"A Mixture Integer GARCH Model with Application to Modeling and Forecasting COVID-19 Counts","year":2025,"lang":"en","type":"article","venue":"Stats","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":"University of Victoria","funders":"Ministry of Higher Education, Malaysia","keywords":"Coronavirus disease 2019 (COVID-19); Autoregressive conditional heteroskedasticity; 2019-20 coronavirus outbreak; Econometrics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Integer (computer science); Mathematics; Computer science; Virology; Internal medicine; Medicine; Volatility (finance)","retraction":null,"screen_n_in":null,"score":{"opus":0.03689804308090749,"gpt":0.3290919629729618,"spread":0.2921939198920543,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002401798,0.00007213274,0.00008523411,0.00007287508,0.00007670116,0.00005476522,0.0001758636,0.00003377318,9.802452e-7],"category_scores_gemma":[0.00003252112,0.00005749521,0.00001072086,0.0002070919,0.00001190954,0.00008119343,0.00009341157,0.00008318022,0.000002455099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003276494,"about_ca_system_score_gemma":0.0001397153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001937059,"about_ca_topic_score_gemma":0.00001513746,"domain_scores_codex":[0.9993955,0.00002161945,0.00008871608,0.000264773,0.00009660231,0.0001327457],"domain_scores_gemma":[0.9995777,0.0000401727,0.00001944994,0.0002148522,0.00005519657,0.00009263383],"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":[0.00003278067,0.00001745119,0.00004819425,0.00009799885,0.00001532701,0.000004094319,0.002021002,0.06345176,0.0005545092,0.4590652,0.002368039,0.4723237],"study_design_scores_gemma":[0.0000931824,0.000009982355,0.000002110766,0.00001783062,0.000002651831,0.000003004545,0.000006897657,0.8935932,0.00003391536,0.10512,0.00105357,0.00006364874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006680878,0.00008916818,0.9920189,0.001248573,0.00002992877,0.0001727645,0.000003119249,0.00005112677,0.005718269],"genre_scores_gemma":[0.2596033,0.000004459204,0.7375475,0.00215671,0.0000118141,0.00002810965,0.000001194478,0.000004693723,0.0006422438],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8301414,"threshold_uncertainty_score":0.2344587,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403479571","doi":"10.3390/stats7040067","title":"Preliminary Test Estimation for Parallel 2-Sampling in Autoregressive Model","year":2024,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; Estimation; Test (biology); STAR model; Sampling (signal processing); Computer science; Statistics; Econometrics; Mathematics; Autoregressive integrated moving average; Engineering; Time series; Geology; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.200054300680119,"gpt":0.460736760953135,"spread":0.2606824602730161,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000254547,0.00009269838,0.0001362396,0.0000584916,0.0000337671,0.00004388933,0.00006569936,0.0000487195,0.00002092457],"category_scores_gemma":[0.003234987,0.00007728287,0.00003091251,0.0000671583,0.00002630735,0.00007340261,0.00002385862,0.00009772964,0.000009670363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004008264,"about_ca_system_score_gemma":0.0000663215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004198642,"about_ca_topic_score_gemma":0.00000304331,"domain_scores_codex":[0.9993045,0.00001864328,0.0002151905,0.0001883911,0.00009680508,0.0001764132],"domain_scores_gemma":[0.9957941,0.003985463,0.00003183817,0.000111881,0.00003827704,0.0000384234],"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":[0.00004240675,0.00008418804,0.00003913771,0.0007299091,0.00001040902,0.00001373242,0.001627778,0.007122193,0.0002099986,0.861953,0.002555492,0.1256117],"study_design_scores_gemma":[0.00007369707,0.00005764018,0.0001236149,0.0001615287,0.000008436592,9.462316e-7,0.00001897634,0.5068481,0.00004226693,0.4925843,0.00002854304,0.00005197942],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004477315,0.0001206715,0.9937823,0.0001745243,0.0001018285,0.0003171588,0.0001321362,0.00008559517,0.0008085059],"genre_scores_gemma":[0.159461,0.000005504437,0.8400435,0.00002247127,0.00002634059,0.0001258083,0.000009688148,0.00001778491,0.0002878057],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4997259,"threshold_uncertainty_score":0.3872816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7117115668","doi":"10.3390/stats9010001","title":"A Proportional Hazards Mixture Cure Model for Subgroup Analysis: Inferential Method and an Application to Colon Cancer Data","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Levamisole; Cure rate; Colorectal cancer; Proportional hazards model; Cancer; Mixture model; Maximum likelihood","retraction":null,"screen_n_in":null,"score":{"opus":0.4864509021177346,"gpt":0.6537123222113409,"spread":0.1672614200936063,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002420861,0.0001499693,0.0005170131,0.000128726,0.00009578479,0.00005407138,0.0003368286,0.0001515957,0.0000407472],"category_scores_gemma":[0.009016977,0.0001289291,0.00006644255,0.000463489,0.00004987945,0.00009616176,0.000212447,0.0001435714,7.95504e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004247276,"about_ca_system_score_gemma":0.0002250989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004604795,"about_ca_topic_score_gemma":0.0006292964,"domain_scores_codex":[0.9980416,0.0002809086,0.0005772476,0.0006415588,0.0002514833,0.0002071431],"domain_scores_gemma":[0.9941525,0.004539355,0.0001778579,0.0007444232,0.000253574,0.0001322285],"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":[0.0009465462,0.0004961445,0.001051787,0.0006188251,0.001512919,0.00000104193,0.0004323133,0.002113787,0.001586136,0.7572876,0.02111911,0.2128339],"study_design_scores_gemma":[0.0003540408,0.00003657032,0.0003630393,0.00001742006,0.0009966388,8.966399e-8,0.00001042497,0.417608,0.0001299083,0.5798044,0.0005863996,0.00009308266],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003221234,0.0000316621,0.9901848,0.001152404,0.0001301793,0.00130872,0.003856336,0.00005172691,0.00006295944],"genre_scores_gemma":[0.03417682,0.00001406199,0.9641041,0.0003498952,0.0001591184,0.0006452684,0.0002063615,0.00001693054,0.0003274886],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4154942,"threshold_uncertainty_score":0.9993305,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4214516580","doi":"10.3390/stats5010014","title":"Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Center for Advancing Translational Sciences; National Institute of Dental and Craniofacial Research; National Institute of Environmental Health Sciences; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Covariate; Pairwise comparison; Inverse probability weighting; Bootstrapping (finance); Weighting; Standard error; Sample size determination; Statistics; Population; Association (psychology); Computer science; Genetic association; Econometrics; Medicine; Machine learning; Psychology; Mathematics; Estimator; Genotype; Biology; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.03386650855273303,"gpt":0.302638456345858,"spread":0.268771947793125,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001315084,0.0001061606,0.0002148536,0.0001191002,0.0001762436,0.000005051202,0.00002660111,0.00001974039,0.0006747028],"category_scores_gemma":[0.00002300027,0.00009883889,0.00004704989,0.00007578774,0.00001980587,0.00004189596,0.00002969854,0.0001889156,0.00003143997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001407654,"about_ca_system_score_gemma":0.00008567231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002217817,"about_ca_topic_score_gemma":0.0001285364,"domain_scores_codex":[0.9992144,0.00005194464,0.0001548113,0.0001966179,0.0001962266,0.0001859652],"domain_scores_gemma":[0.9997221,0.00006561124,0.00002322439,0.0001054684,0.0000353904,0.00004818245],"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":[0.01214404,0.004695576,0.007346397,0.000654874,0.003120788,0.1629378,0.03994474,0.1130117,0.02333833,0.07212088,0.006767403,0.5539175],"study_design_scores_gemma":[0.2453089,0.08328955,0.007202133,0.001218441,0.002831448,0.02989494,0.1631777,0.1003484,0.01352653,0.3218389,0.02645413,0.004908843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931883,0.003629962,0.00007030906,0.000444387,0.0002423881,0.000371864,0.0003083198,0.00004372325,0.001700731],"genre_scores_gemma":[0.9978607,0.00002461462,0.0002447997,0.0009212662,0.00008406449,0.0001358613,0.0001043723,0.00001649358,0.0006078575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5490086,"threshold_uncertainty_score":0.7387522,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413129810","doi":"10.3390/stats8030071","title":"Individual Homogeneity Learning in Density Data Response Additive Models","year":2025,"lang":"en","type":"article","venue":"Stats","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":"McMaster University","funders":"Humanities and Social Science Fund of Ministry of Education of China; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Covariate; Estimator; Homogeneity (statistics); Bivariate analysis; Mathematics; Bivariate data; Additive model; Compositional data; Spline (mechanical); Cluster analysis; Euclidean space; Hierarchical clustering; Computer science; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.06166643278012812,"gpt":0.3340903468106706,"spread":0.2724239140305425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002257815,0.0001189583,0.0001780607,0.0001873118,0.0001185134,0.0001007728,0.00129599,0.00007427529,0.000004125322],"category_scores_gemma":[0.0003173047,0.0001177803,0.00002594685,0.0005338907,0.00004278664,0.0006453322,0.001444261,0.0003257561,0.00000655649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004113228,"about_ca_system_score_gemma":0.0003139261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006946307,"about_ca_topic_score_gemma":0.0001057689,"domain_scores_codex":[0.9979456,0.0008185048,0.000182773,0.0005725494,0.0001963157,0.0002842242],"domain_scores_gemma":[0.9984882,0.0004293934,0.00005061604,0.0009165694,0.00005404716,0.00006113512],"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":[0.0002232248,0.0001067491,0.0009145272,0.00001471743,0.00004755514,0.000151739,0.003205957,0.0005701581,0.0005138101,0.1409872,0.005515853,0.8477485],"study_design_scores_gemma":[0.001039072,0.0001000971,0.058064,0.0001146906,0.00002216664,0.00001235095,0.0001015198,0.4730833,0.003288759,0.4580453,0.005661768,0.0004669176],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05090189,0.000155293,0.9461967,0.000589037,0.0001609647,0.0001215313,0.0000610834,0.00008716537,0.001726274],"genre_scores_gemma":[0.6053345,0.00002359619,0.3935502,0.0004708958,0.00001683294,0.000006674887,0.00002775237,0.000005318919,0.0005641345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8472815,"threshold_uncertainty_score":0.480294,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417331967","doi":"10.3390/stats8040118","title":"Korovkin-Type Approximation Theorems for Statistical Gauge Integrable Functions of Two Variables","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Approximation Theory and Sequence Spaces","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Integrable system; Gauge (firearms); Banach space; Linear operators; Robustness (evolution); Limit (mathematics); Gauge theory","retraction":null,"screen_n_in":null,"score":{"opus":0.04596983138779122,"gpt":0.3654803443482419,"spread":0.3195105129604507,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007182789,0.0001199524,0.0002343707,0.0001068512,0.0001381985,0.0000273838,0.0001263141,0.00006087685,0.0004372994],"category_scores_gemma":[0.00123213,0.0001013707,0.00004920001,0.0002713212,0.0001876049,0.0001383543,0.00002584782,0.00009271444,0.00001046128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003889246,"about_ca_system_score_gemma":0.0001215746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000019223,"about_ca_topic_score_gemma":0.00001598186,"domain_scores_codex":[0.9990613,0.00009663185,0.0003408132,0.0001830736,0.0001306814,0.0001874818],"domain_scores_gemma":[0.9980297,0.00130208,0.0001402013,0.0002422177,0.0002509516,0.00003484884],"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":[0.0001153009,0.0001003507,0.00002684564,0.0002834956,0.00004866634,2.642566e-7,0.0004087478,0.00003674939,0.001649314,0.9885983,0.00672215,0.002009795],"study_design_scores_gemma":[0.0004644482,0.00008393449,0.00001028551,0.00007351145,0.00006886298,0.000001034162,0.0009773189,0.005693711,0.006903032,0.9843898,0.001240656,0.00009335423],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01630108,0.00003227249,0.954403,0.0001516743,0.0002848011,0.0005288073,0.0002185175,0.00007747636,0.02800237],"genre_scores_gemma":[0.8015938,0.000008426508,0.1925896,0.00006959805,0.00004044076,0.00009599982,0.0001612681,0.00001940584,0.005421511],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7852927,"threshold_uncertainty_score":0.4788122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410444692","doi":"10.3390/stats8020039","title":"Theoretical Advancements in Small Area Modeling: A Case Study with the CHILD Cohort","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Manitoba","funders":"Canadian Institutes of Health Research","keywords":"Cohort; Medicine; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.03237891762714051,"gpt":0.3657671923366269,"spread":0.3333882747094864,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007012259,0.0000668177,0.0001159161,0.00003333353,0.0004729,0.00004164362,0.0001301302,0.00002547582,0.00007325036],"category_scores_gemma":[0.00008393446,0.00004071051,0.00001579634,0.0002221277,0.0001562548,0.00004252915,0.00003656487,0.0001383761,0.00000201917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007031491,"about_ca_system_score_gemma":0.0001879633,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01438069,"about_ca_topic_score_gemma":0.1232782,"domain_scores_codex":[0.9990745,0.0001672392,0.0001338467,0.0001548042,0.000155508,0.0003140968],"domain_scores_gemma":[0.9996013,0.0001408892,0.00001806588,0.0001402932,0.00003581229,0.00006363692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007211255,0.0002429706,0.5555477,0.00002258238,0.00003074243,0.0004719641,0.02671812,0.0004753862,1.640551e-8,0.4134979,0.0001568389,0.002763658],"study_design_scores_gemma":[0.003200486,0.0003506507,0.07592874,0.000292559,0.0001071675,0.00002351379,0.8611245,0.01108808,0.000001317381,0.03568877,0.01171994,0.0004743306],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9501433,0.00009388088,0.0006271595,0.007887754,0.0001022592,0.0008005099,0.000003650344,0.00001996484,0.04032157],"genre_scores_gemma":[0.9974002,0.00004023638,0.00004332736,0.002090191,0.00002217865,0.00007087095,7.268815e-7,0.000003932704,0.0003283546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8344063,"threshold_uncertainty_score":0.9921826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414981511","doi":"10.3390/stats8040092","title":"Predictions of War Duration","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Defense, Military, and Policy Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"Poisson distribution; Duration (music); Measure (data warehouse); Work (physics); Probability distribution; Investment (military)","retraction":null,"screen_n_in":null,"score":{"opus":0.03141607107390611,"gpt":0.2566997352297068,"spread":0.2252836641558007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009312837,0.00004273971,0.0001261492,0.0001190673,0.00005955745,0.000003019655,0.00004491186,0.00002398383,0.0000594059],"category_scores_gemma":[0.00008014344,0.00004891054,0.00004009028,0.0001450572,0.00003774367,0.00005104109,0.00001718398,0.00003181969,0.00009087934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001999132,"about_ca_system_score_gemma":0.000009993287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00031979,"about_ca_topic_score_gemma":0.0001176749,"domain_scores_codex":[0.999541,0.000003849626,0.0002585413,0.0001019937,0.00001005687,0.00008457353],"domain_scores_gemma":[0.9997558,0.00002781601,0.00006143714,0.0001220283,0.00002024584,0.00001266488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00001621862,0.0000766509,0.09227696,0.00008517758,0.000139642,4.993638e-7,0.002837816,0.0001018091,0.00001938539,0.7977577,0.1053642,0.001323932],"study_design_scores_gemma":[0.000470152,0.00007391553,0.3727708,0.00002256408,0.00001261481,4.055029e-7,0.0005239849,0.0008189837,0.0001983294,0.2755188,0.3494524,0.0001370289],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2426319,0.009979329,0.006829343,0.002905936,0.001408898,0.0002198908,0.0009143245,0.00005789373,0.7350525],"genre_scores_gemma":[0.9935572,0.0005816011,0.0001782874,0.0001484664,0.00003871938,0.000009604847,0.000009392275,0.000003193062,0.005473511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7509253,"threshold_uncertainty_score":0.1994514,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4396925359","doi":"10.3390/stats7020027","title":"Multivariate and Matrix-Variate Logistic Models in the Real and Complex Domains","year":2024,"lang":"en","type":"article","venue":"Stats","topic":"Neural Networks and Applications","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":"McGill University","funders":"","keywords":"Random variate; Multivariate statistics; Logistic regression; Multivariate analysis; Mathematics; Econometrics; Statistics; Computer science; Random variable","retraction":null,"screen_n_in":null,"score":{"opus":0.08053115015664743,"gpt":0.3442012222432785,"spread":0.2636700720866311,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001563668,0.00006234521,0.00006275163,0.00002862206,0.00007759098,0.0002306062,0.0001861317,0.00001715103,0.000001406802],"category_scores_gemma":[0.000002885993,0.00004052696,0.000009865793,0.0001745945,0.00004431536,0.00014724,0.0001096922,0.00008478324,0.000003858885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006538306,"about_ca_system_score_gemma":0.00001142419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002651021,"about_ca_topic_score_gemma":0.00006447901,"domain_scores_codex":[0.9994459,0.00004157038,0.00009244283,0.0002149833,0.00007239933,0.0001326724],"domain_scores_gemma":[0.9995505,0.0002264996,0.00001316638,0.0001731729,0.000006512005,0.00003021056],"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":[0.00000152293,0.00001040042,0.00003057797,0.0000119345,0.00000340396,0.00002802515,0.001095202,0.0009444803,0.000270296,0.9861911,0.0005504112,0.01086266],"study_design_scores_gemma":[0.00008674972,0.00001324619,0.01006045,0.00001038981,0.000002785851,0.00001161458,0.00001958247,0.7483026,0.000001356413,0.2403213,0.001117581,0.00005236333],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08340027,0.000592978,0.9064454,0.005981206,0.0001467876,0.0003743155,0.00002383383,0.0001428779,0.002892356],"genre_scores_gemma":[0.9908544,0.0001432351,0.008729172,0.000155262,0.00003071661,0.00001674877,0.000002420485,0.00000364515,0.00006441407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9074541,"threshold_uncertainty_score":0.222374,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415283929","doi":"10.3390/stats8040097","title":"Goodness-of-Fit Tests via Entropy-Based Density Estimation Techniques","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"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; Entropy (arrow of time); Statistical hypothesis testing; Range (aeronautics); Inference; Statistical inference; Density estimation; Empirical likelihood","retraction":null,"screen_n_in":null,"score":{"opus":0.09158050146229392,"gpt":0.4304738654231152,"spread":0.3388933639608213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002827456,0.00009790059,0.0002109927,0.00007219423,0.00004419623,0.0000136209,0.0001002946,0.0000587911,0.00008027467],"category_scores_gemma":[0.002256891,0.00008528824,0.00003723285,0.000181334,0.00007222847,0.00003202462,0.00003351788,0.00008604735,0.000007124831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003294765,"about_ca_system_score_gemma":0.00007453551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000320564,"about_ca_topic_score_gemma":0.00000737348,"domain_scores_codex":[0.9992328,0.000082542,0.0002616128,0.0001449486,0.0001410691,0.0001370344],"domain_scores_gemma":[0.9982616,0.001210883,0.0001000906,0.0002430956,0.0001481697,0.00003620741],"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":[0.0000386485,0.0002063265,0.002491832,0.0004814543,0.00002190092,0.000005620124,0.00007972792,0.000005008227,0.0113793,0.7477389,0.001637202,0.2359141],"study_design_scores_gemma":[0.0001324892,0.000062481,0.004506416,0.0001415232,0.00003363387,4.770766e-7,0.000008108875,0.0107806,0.1228905,0.861253,0.0001065306,0.00008423386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03329074,0.00001071406,0.9636311,0.000122171,0.00008577308,0.0002031043,0.00002412995,0.0001056826,0.00252662],"genre_scores_gemma":[0.4178745,9.752646e-7,0.5819681,0.00006359363,0.000006709448,0.00001227874,0.000002709871,0.000005095237,0.00006605357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3845837,"threshold_uncertainty_score":0.3477953,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413060023","doi":"10.3390/stats8030065","title":"Local Stochastic Correlation Models for Derivative Pricing","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Geometric Brownian motion; Brownian motion; Derivative (finance); Stochastic game; Econometrics; Simple (philosophy); Asset (computer security); Correlation; Multivariate statistics; Stock (firearms); Stochastic process; Mathematical optimization; Mathematics; Computer science; Mathematical economics; Economics; Applied mathematics; Financial economics; Diffusion process; Statistics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03293134452748687,"gpt":0.2550547446506732,"spread":0.2221234001231863,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001050618,0.00007979879,0.0001697795,0.0001147974,0.0001446817,0.00002151628,0.00009569982,0.00005321071,0.000008260347],"category_scores_gemma":[0.0001048551,0.00009657149,0.00004388147,0.0002720244,0.00003969183,0.0001159239,0.00002626447,0.00005870158,0.0000429968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007859521,"about_ca_system_score_gemma":0.0000395549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000562212,"about_ca_topic_score_gemma":0.00001014521,"domain_scores_codex":[0.9992872,8.420602e-7,0.0002871187,0.0002430558,0.00001535158,0.0001663553],"domain_scores_gemma":[0.9995555,0.0001078594,0.0001243475,0.0001271968,0.00005911571,0.00002596017],"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":[0.00001431862,0.00002246216,0.00002632582,0.00002003331,0.00001176904,4.883602e-8,0.0002265735,0.02186056,0.000002725554,0.9725143,0.0001951945,0.005105643],"study_design_scores_gemma":[0.0002207289,0.0000186886,0.0005078875,0.00001325277,0.000003981072,1.442377e-7,0.00006067222,0.3170621,0.00000996423,0.6812432,0.0007890013,0.00007041007],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005463159,0.0005316838,0.9904207,0.0003256924,0.0002070179,0.0004153049,0.0001376668,0.00003371323,0.007381912],"genre_scores_gemma":[0.9909546,0.000004905172,0.008064611,0.0002111721,0.0000303712,0.0002498979,0.00002751491,0.00001009646,0.0004468827],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9904082,"threshold_uncertainty_score":0.3938071,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416322124","doi":"10.3390/stats8040110","title":"Prediction Inferences for Finite Population Totals Using Longitudinal Survey Data","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Covariate; Regression analysis; Sampling design; Sampling (signal processing); Regression; Cluster sampling; Correlation; Sample (material); Poisson sampling","retraction":null,"screen_n_in":null,"score":{"opus":0.5194947094376512,"gpt":0.5077778827261674,"spread":0.01171682671148377,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009512042,0.00009568421,0.0001898078,0.00006817978,0.0001210886,0.0000638737,0.0001697218,0.00005669415,0.00004897456],"category_scores_gemma":[0.007094061,0.00008427211,0.00001975543,0.0001865211,0.00003139813,0.0001628287,0.00009381279,0.00006419864,0.00000143839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002786863,"about_ca_system_score_gemma":0.00006465769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003739766,"about_ca_topic_score_gemma":0.0003034878,"domain_scores_codex":[0.9989967,0.0001525692,0.0002988403,0.0002643479,0.0001230238,0.0001645388],"domain_scores_gemma":[0.9958668,0.003528159,0.00009330941,0.000354686,0.0001244524,0.00003261094],"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":[0.0001424369,0.0001292425,0.3958181,0.0004734902,0.0001057759,0.000001903006,0.000120798,0.00002602002,0.0001051793,0.4803757,0.003651825,0.1190496],"study_design_scores_gemma":[0.0001652541,0.00003316599,0.2753353,0.00009030326,0.00004556769,4.284879e-7,0.00001563256,0.05092521,0.00003265612,0.673233,0.00005044078,0.00007306033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02818009,0.00001990428,0.9682444,0.00002416884,0.0003576181,0.0002648712,0.002494194,0.00003995968,0.0003748296],"genre_scores_gemma":[0.4407158,0.000004541619,0.5587291,0.00001516752,0.00004504664,0.000009821212,0.000401472,0.000006766292,0.00007221369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4125358,"threshold_uncertainty_score":0.8492767,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}