{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":17,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":17,"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":"6dd8a7f9b8c8","filters":{"venue":"Bayesian Analysis"}},"results":[{"id":"W4246027503","doi":"10.1214/ba/1339616472","title":"Flexible paleoclimate age-depth models using an autoregressive gamma process","year":2011,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3800,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Queen's University; Queen's University Belfast","keywords":"Radiocarbon dating; Autoregressive model; Outlier; Paleoclimatology; Prior probability; Markov chain Monte Carlo; Geology; Sampling (signal processing); Physical geography; Computer science; Statistics; Paleontology; Artificial intelligence; Bayesian probability; Mathematics; Geography; Climate change; Oceanography","retraction":null,"screen_n_in":null,"score":{"opus":0.1055040289042663,"gpt":0.3245205179113725,"spread":0.2190164890071061,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006827576,0.0003996902,0.0007067616,0.0007817799,0.0002893116,0.0003038704,0.001560448,0.0001861498,0.00009437188],"category_scores_gemma":[0.00001741779,0.0003123986,0.0004373481,0.002241179,0.0001065164,0.001696978,0.0001879054,0.0002258195,0.00001140597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005105141,"about_ca_system_score_gemma":0.0001351325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002004112,"about_ca_topic_score_gemma":0.0001551929,"domain_scores_codex":[0.9968482,0.0003358649,0.0005176683,0.001094817,0.0004935025,0.0007099123],"domain_scores_gemma":[0.9974446,0.00002935535,0.0003284971,0.001558783,0.0002078148,0.000430891],"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":[0.0001648464,0.002431377,0.01671604,0.0003271608,0.006112124,0.002273351,0.06684305,0.03861808,0.002166072,0.5049411,0.0002029183,0.3592038],"study_design_scores_gemma":[0.0001945254,0.00005383668,0.0009666468,0.00002163948,0.0006346051,0.00001705622,0.00006304671,0.8494728,0.002509735,0.14559,0.00001062267,0.0004654513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003188377,0.000126458,0.9828826,0.00004711693,0.0001026247,0.0001789293,0.000006086117,0.0004222983,0.01304555],"genre_scores_gemma":[0.5034998,0.00001035377,0.4960066,0.0001421145,0.00004925174,0.00001394597,0.000006629544,0.00002263528,0.0002486967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8108547,"threshold_uncertainty_score":0.9999328,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2920804790","doi":"10.1214/20-ba1221","title":"Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)","year":2020,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1511,"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":"Markov chain Monte Carlo; Convergence (economics); Monte Carlo method; Bayesian probability; Variance (accounting); Markov chain; TRACE (psycholinguistics); Rank (graph theory)","retraction":null,"screen_n_in":null,"score":{"opus":0.04196391283684291,"gpt":0.3385584988034436,"spread":0.2965945859666007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000408966,0.0001543594,0.0004668159,0.000123482,0.0001225224,0.00006214481,0.0001180764,0.00006755444,0.00006418292],"category_scores_gemma":[0.0003149287,0.00008973282,0.0001375793,0.0009011069,0.00005886146,0.0002397862,0.00003222485,0.00004342872,1.04706e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001203763,"about_ca_system_score_gemma":0.00004108965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001656728,"about_ca_topic_score_gemma":0.00007065314,"domain_scores_codex":[0.9988281,0.000133035,0.0003955567,0.0003192087,0.000168042,0.0001560948],"domain_scores_gemma":[0.9989339,0.0001214867,0.000284908,0.000259565,0.0002270849,0.0001730145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001520926,0.001264256,0.6127262,0.008385792,0.01119678,0.00003827273,0.0411183,0.008613798,0.02073371,0.1658643,0.00464024,0.1238974],"study_design_scores_gemma":[0.0007947127,0.0001466055,0.0004466433,0.00004397288,0.002323974,0.000001398021,0.001706129,0.989767,0.002182388,0.00129831,0.001014086,0.0002748281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005802514,0.00004094224,0.9932067,0.0004584368,0.0000189443,0.0002434809,0.00001593384,0.00004067802,0.0001723093],"genre_scores_gemma":[0.786557,0.00001879491,0.2128602,0.0002004675,0.00005570567,0.00002039538,0.00004967213,0.00002645149,0.0002113351],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9811531,"threshold_uncertainty_score":0.3659198,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158679941","doi":"10.1214/06-ba129","title":"Checking for prior-data conflict","year":2006,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":185,"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; Computer science; Inference; Context (archaeology); Sampling (signal processing); Data mining; Bayesian probability; Econometrics; Machine learning; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0541998460996983,"gpt":0.3024496183704897,"spread":0.2482497722707914,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005107261,0.0001699565,0.0003228521,0.0002982639,0.0001926645,0.0003419657,0.001848501,0.00008174319,0.00003063923],"category_scores_gemma":[0.00003793463,0.0001624152,0.0002189436,0.001345772,0.00003508512,0.0004161359,0.0002684608,0.00009010209,0.00001859514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002798639,"about_ca_system_score_gemma":0.00007273639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003064228,"about_ca_topic_score_gemma":0.0001588357,"domain_scores_codex":[0.9982624,0.00003949358,0.0003519605,0.000728946,0.000250125,0.0003670534],"domain_scores_gemma":[0.9979183,0.00009085931,0.0001326411,0.001651949,0.0001151994,0.00009106755],"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":[0.00003566757,0.0005916895,0.01843822,0.00009288461,0.002658829,0.00005020541,0.0007133877,0.01530076,0.002054558,0.6845429,0.03405957,0.2414613],"study_design_scores_gemma":[0.0001947699,0.00002148812,0.001828004,0.000006881165,0.0003448374,0.000002252028,0.000007928278,0.9846027,0.0003389567,0.006543396,0.005861385,0.0002474307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008137019,0.0002352314,0.9949442,0.0008067035,0.00006436405,0.0001134747,0.00002522068,0.00020625,0.002790881],"genre_scores_gemma":[0.8037228,0.000009112762,0.1948544,0.0002378668,0.0001281184,0.00001423534,0.000115672,0.000009892434,0.0009078992],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9693019,"threshold_uncertainty_score":0.6623099,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2071558725","doi":"10.1214/12-ba719","title":"Simulation-based Regularized Logistic Regression","year":2012,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Booth University College","funders":"Engineering and Physical Sciences Research Council","keywords":"Mathematics; Logistic regression; Estimator; Markov chain Monte Carlo; Regularization (linguistics); Maximum a posteriori estimation; Computer science; Statistics; Maximum likelihood; Artificial intelligence; Monte Carlo method","retraction":null,"screen_n_in":null,"score":{"opus":0.03629584693375288,"gpt":0.3235307993996509,"spread":0.287234952465898,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000863629,0.0002060002,0.0003966535,0.0004954059,0.0001683572,0.0001185138,0.0005820331,0.0001263833,0.0001385692],"category_scores_gemma":[0.0002706029,0.0001617709,0.0003889855,0.002171068,0.00004439415,0.0003893021,0.0000846284,0.0001275114,0.00003997318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005095555,"about_ca_system_score_gemma":0.0000489229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001696307,"about_ca_topic_score_gemma":0.000003391827,"domain_scores_codex":[0.9981031,0.0003544879,0.0003231194,0.0004055223,0.0003674394,0.0004462758],"domain_scores_gemma":[0.997807,0.0005488578,0.0001746148,0.001098898,0.00009128287,0.000279336],"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":[0.00007544621,0.000905749,0.03853669,0.00008020579,0.001922499,0.0000648953,0.001391978,0.2342683,0.001796742,0.419889,0.001041996,0.3000265],"study_design_scores_gemma":[0.0002239555,0.00001246172,0.002878271,0.000008267373,0.0003990298,6.26774e-7,0.000002119105,0.988299,0.0003199657,0.007155827,0.0004753205,0.0002252322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000261133,0.0001805897,0.997268,0.0004329344,0.0001424975,0.00009764091,0.000002183106,0.00017413,0.001440946],"genre_scores_gemma":[0.5470099,0.00000105651,0.4523227,0.0002830414,0.00006506569,0.000005568087,0.000008034243,0.000006685078,0.0002979515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7540306,"threshold_uncertainty_score":0.6596828,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2085459152","doi":"10.1214/09-ba419","title":"Prediction of pregnancy: a joint model for longitudinal and binary data","year":2009,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; California HIV/AIDS Research Program","keywords":"Population; Linear model; Bayesian probability; Pregnancy; Generalized linear model; Random effects model; Statistics; Longitudinal study; Binary data; Joint (building); Computer science; Binary number; Mathematics; Medicine; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2083462160104008,"gpt":0.3814452948409056,"spread":0.1730990788305047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038418,0.0001279571,0.0004641771,0.0002053601,0.00006141882,0.00002343112,0.0001820404,0.00006711438,0.00003717304],"category_scores_gemma":[0.0006040742,0.0001088909,0.0001139035,0.0004166852,0.000056832,0.0001193359,0.00005688058,0.00006194897,3.132557e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001288988,"about_ca_system_score_gemma":0.00003143183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007858477,"about_ca_topic_score_gemma":0.00001226294,"domain_scores_codex":[0.9988097,0.0000501932,0.0004171871,0.0003825423,0.0001705347,0.0001698695],"domain_scores_gemma":[0.9987326,0.0002504598,0.0001683837,0.0006792651,0.00008576374,0.00008353084],"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":[0.0001298178,0.0007310771,0.0063074,0.0005894336,0.001766113,0.0000127397,0.0006878498,0.0001613101,0.001789101,0.7970968,0.002744407,0.187984],"study_design_scores_gemma":[0.0001526199,0.00008572142,0.006971782,0.00005735594,0.001384182,0.000001165543,0.00001356138,0.6055677,0.00005202146,0.3856467,0.00000202542,0.00006515394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001166391,0.0001356882,0.997236,0.0001883628,0.00001149466,0.0002020723,0.0007375065,0.00003059751,0.0002919168],"genre_scores_gemma":[0.3991764,0.00002783418,0.6006487,0.00001056228,0.00001506346,0.000006905892,0.00005070913,0.000005597199,0.00005821542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6054064,"threshold_uncertainty_score":0.4440441,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2021803007","doi":"10.1214/13-ba824","title":"Hypothesis Assessment and Inequalities for Bayes Factors and Relative Belief Ratios","year":2013,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":40,"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":"Bayes factor; Bayes' rule; Bayes' theorem; Bayes error rate; Bayesian probability; Bayesian programming; A priori and a posteriori; Mathematics; Bayesian inference; Econometrics; Statistics; Point estimation; Computer science; Bayes classifier","retraction":null,"screen_n_in":null,"score":{"opus":0.07725196141600667,"gpt":0.3691396893848857,"spread":0.2918877279688791,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003981225,0.000236176,0.0006297801,0.0002452086,0.0001914002,0.0001777661,0.00008679224,0.00009747547,0.000495672],"category_scores_gemma":[0.00137137,0.0001748595,0.0001477267,0.0003387251,0.0001385632,0.0002282019,0.00004650129,0.00009830993,0.000002339815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000034519,"about_ca_system_score_gemma":0.00002841346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001757754,"about_ca_topic_score_gemma":0.00008647247,"domain_scores_codex":[0.9985334,0.0002151777,0.0004335092,0.0003733084,0.0001907108,0.000253945],"domain_scores_gemma":[0.9936263,0.005656944,0.0001765077,0.000250131,0.0001297666,0.0001603587],"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":[0.000006654009,0.00006908805,0.07943192,0.0001399003,0.001303043,0.000001013099,0.001353873,2.956942e-7,0.0001167176,0.8917952,0.000486565,0.02529576],"study_design_scores_gemma":[0.0002178793,0.00009179186,0.1183623,0.00001945799,0.00135365,6.966544e-7,0.001248787,0.008039461,0.0001344943,0.8702401,0.00003905827,0.00025232],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05639759,0.00004823536,0.9415304,0.0004321957,0.00001707395,0.0003993551,0.000102016,0.00004154141,0.001031587],"genre_scores_gemma":[0.4550173,0.0000203724,0.5445763,0.00004224891,0.00001919727,0.00008842976,0.000007557204,0.00001365377,0.0002149498],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3986197,"threshold_uncertainty_score":0.7130563,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077512673","doi":"10.1214/07-ba209","title":"Improving classification when a class hierarchy is available using a hierarchy-based prior","year":2007,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":33,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hierarchy; Multinomial logistic regression; Class hierarchy; Computer science; Class (philosophy); Machine learning; Artificial intelligence; Multinomial distribution; Bayesian probability; Tree (set theory); Data mining; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.03222386111863169,"gpt":0.2757000272695841,"spread":0.2434761661509524,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001020241,0.0002750694,0.0003880284,0.001881816,0.0003955406,0.0005062144,0.00138614,0.0002082427,0.0003978417],"category_scores_gemma":[0.000108897,0.0002687766,0.0003562243,0.004043818,0.0001507999,0.0007715565,0.0002030478,0.0002551402,0.0001862748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004566361,"about_ca_system_score_gemma":0.0002404266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002604588,"about_ca_topic_score_gemma":0.00007222577,"domain_scores_codex":[0.997104,0.00008442237,0.000655738,0.0009307492,0.0006031868,0.0006218944],"domain_scores_gemma":[0.9973353,0.0001476378,0.0004509182,0.001688691,0.0001953619,0.0001821662],"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":[0.0001142314,0.0008372234,0.06725814,0.0001566827,0.001720859,0.0001053697,0.002341725,0.0002581308,0.09966429,0.2137783,0.01151895,0.602246],"study_design_scores_gemma":[0.0004261737,0.00004897438,0.003992012,0.00001304357,0.0003362149,0.000003115822,0.0002111167,0.9663775,0.01684521,0.002210925,0.00907199,0.0004637044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005505625,0.0001401087,0.9868657,0.002653972,0.00007227575,0.0002356449,0.000005182279,0.0007410078,0.003780468],"genre_scores_gemma":[0.696539,0.000006364703,0.3011951,0.0005787318,0.00003598466,0.00001730749,0.00001422289,0.00001642582,0.001596859],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9661194,"threshold_uncertainty_score":0.9999765,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2508625972","doi":"10.1214/16-ba1024","title":"Optimal Robustness Results for Relative Belief Inferences and the Relationship to Prior-Data Conflict","year":2016,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"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":"Robustness (evolution); Inference; Computer science; Bayesian probability; Econometrics; Bayesian inference; Prior probability; Data mining; Machine learning; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1422005404427989,"gpt":0.3996265789048998,"spread":0.2574260384621009,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002448329,0.0002096227,0.0005743299,0.0002191215,0.0003105125,0.000114596,0.0005279881,0.0001114427,0.0000920618],"category_scores_gemma":[0.02457937,0.0001071957,0.000139963,0.0008273224,0.0003364103,0.0002144376,0.0002097413,0.0001179214,0.00001096794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002774252,"about_ca_system_score_gemma":0.00005928295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004402356,"about_ca_topic_score_gemma":0.0001604397,"domain_scores_codex":[0.997752,0.0004423932,0.0006374603,0.0006027257,0.0002682143,0.0002972497],"domain_scores_gemma":[0.9761448,0.02221931,0.0002557854,0.001020593,0.0001926497,0.0001668535],"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":[0.0004469134,0.00003397258,0.00264073,0.00002278912,0.0006391701,0.000001721501,0.0008437576,0.00001610433,0.000005136232,0.9703493,0.001409985,0.02359045],"study_design_scores_gemma":[0.00445455,0.0001896414,0.03418851,0.0001947865,0.004293602,0.000004591404,0.0004699276,0.05703742,0.00004257804,0.8966386,0.001877808,0.0006079761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00238712,0.00004201356,0.9882323,0.006945963,0.00003301872,0.0005507781,0.0006928549,0.00004447021,0.00107149],"genre_scores_gemma":[0.2949346,0.0000179786,0.7039902,0.00006625496,0.00005924097,0.00007020316,0.00002615274,0.00001353879,0.0008218805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2925475,"threshold_uncertainty_score":0.983637,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963124168","doi":"10.1214/13-ba821","title":"On Asymptotic Properties and Almost Sure Approximation of the Normalized Inverse-Gaussian Process","year":2013,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Analysis of environmental and stochastic processes","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Quantile; Gaussian process; Applied mathematics; Central limit theorem; Gaussian; Inverse; Inverse Gaussian distribution; Law of large numbers; Asymptotic analysis; Gaussian random field; Statistical physics; Mathematical analysis; Statistics; Distribution (mathematics); Random variable","retraction":null,"screen_n_in":null,"score":{"opus":0.006799227602229027,"gpt":0.1826566516096504,"spread":0.1758574240074214,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001157666,0.0001567554,0.0002643591,0.00007883109,0.0001672801,0.00003545751,0.0002534501,0.00005232457,0.00186965],"category_scores_gemma":[0.00005488376,0.00009206394,0.0001424467,0.0008223649,0.0003635933,0.0002573439,0.0001075265,0.00007679698,0.0000895776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004129373,"about_ca_system_score_gemma":0.00000643578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000480779,"about_ca_topic_score_gemma":0.0002148994,"domain_scores_codex":[0.9988113,0.00005152549,0.0002615844,0.0002894882,0.0004104902,0.0001756042],"domain_scores_gemma":[0.9994229,0.00002300656,0.00017839,0.000284092,0.000009753001,0.00008183402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007997888,0.0006625805,0.9051194,0.000200172,0.001586041,0.000002127237,0.003499323,0.06395494,0.0164198,0.0007004865,0.000864658,0.006910544],"study_design_scores_gemma":[0.0006058954,0.0001308116,0.699252,0.00006414513,0.002242822,0.000004353654,0.001898217,0.2808495,0.00933485,0.005027527,0.00004112453,0.0005486649],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871489,0.00003671029,0.006423041,0.0006001742,0.00001061344,0.0002760328,0.000003717969,0.00001582475,0.005484923],"genre_scores_gemma":[0.9987535,0.000008141767,0.0002805439,0.0001808517,0.000006169046,0.00003544808,0.000005895515,0.000007977499,0.0007214799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2168946,"threshold_uncertainty_score":0.9990427,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3204314768","doi":"10.1214/21-ba1290","title":"Functional Central Limit Theorems for Stick-Breaking Priors","year":2021,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Mathematics; Central limit theorem; Dirichlet process; Applied mathematics; Dirichlet distribution; Limit (mathematics); Prior probability; Mathematical analysis; Bayesian probability; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01910684797705753,"gpt":0.2575463279633895,"spread":0.238439479986332,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004754819,0.0002002378,0.0003934554,0.000259456,0.0002180906,0.0002811895,0.0004607478,0.0001027985,0.0001838072],"category_scores_gemma":[0.0001324067,0.0001840101,0.0006606864,0.001757629,0.00004018842,0.0002624474,0.0001326011,0.0001264774,0.000009745888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006060253,"about_ca_system_score_gemma":0.0001866224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001519907,"about_ca_topic_score_gemma":0.00005746678,"domain_scores_codex":[0.9979783,0.0001745623,0.0003282935,0.0006933384,0.0003195275,0.000505971],"domain_scores_gemma":[0.9984689,0.0002484937,0.0001215868,0.0007489591,0.0002048794,0.0002071763],"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":[0.00001474639,0.000114871,0.001362973,0.00002060904,0.001217782,0.000042088,0.0005246479,0.0007799933,0.0008847753,0.8505992,0.001366758,0.1430715],"study_design_scores_gemma":[0.0008390943,0.00006114718,0.02038559,0.00002627449,0.002055862,0.00004588317,0.0001003448,0.7849555,0.00535456,0.1773883,0.007999878,0.0007875329],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004590394,0.0002449343,0.9947339,0.001517321,0.000311575,0.0001126286,0.00001204165,0.0001085648,0.002499953],"genre_scores_gemma":[0.3392942,0.00001406127,0.6584479,0.0006190912,0.0001976857,0.00001974952,0.0000338665,0.00001335852,0.001360093],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7841755,"threshold_uncertainty_score":0.7503716,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403132674","doi":"10.1214/24-ba1469","title":"Fast Power Curve Approximation for Posterior Analyses","year":2024,"lang":"es","type":"article","venue":"Bayesian Analysis","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":"University of Waterloo; McGill University","funders":"","keywords":"Mathematics; Econometrics; Applied mathematics; Computer science; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.06587582818423708,"gpt":0.3780268598063007,"spread":0.3121510316220636,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002113898,0.0004286299,0.000958322,0.002304364,0.0002250808,0.002166551,0.0007220138,0.0002488076,0.001412584],"category_scores_gemma":[0.001197893,0.0003225341,0.001548431,0.006686308,0.0001405822,0.0004726809,0.00009944165,0.0001891854,0.0004208112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001312914,"about_ca_system_score_gemma":0.0001915957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003524997,"about_ca_topic_score_gemma":0.00001114715,"domain_scores_codex":[0.9959546,0.0002086652,0.00114091,0.001168029,0.001007889,0.0005199185],"domain_scores_gemma":[0.9967704,0.001460819,0.0002085839,0.0009408244,0.0003730787,0.0002462458],"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":[0.000522974,0.001081448,0.009119515,0.002406281,0.04553346,0.0003342804,0.008257113,0.3768035,0.01251374,0.1332595,0.06166742,0.3485008],"study_design_scores_gemma":[0.0001803377,0.0001271124,0.002959068,0.0001363165,0.006111257,0.0000068829,0.0004349809,0.9762415,0.0002922791,0.004341251,0.008653156,0.0005158758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003241775,0.003695602,0.9887223,0.001049693,0.0006199938,0.0004473475,0.0003578714,0.0001964433,0.001668927],"genre_scores_gemma":[0.9688539,0.00004957669,0.02584231,0.00007785758,0.0002138974,0.00006119594,0.00009839819,0.00004644178,0.004756446],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9656121,"threshold_uncertainty_score":0.9999227,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3196986231","doi":"10.1214/24-ba1428","title":"Scalable Spatiotemporally Varying Coefficient Modeling with Bayesian Kernelized Tensor Regression","year":2024,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Gibbs sampling; Markov chain Monte Carlo; Covariance; Covariate; Bayesian inference; Hyperparameter; Mathematics; Computer science; Kernel (algebra); Bayesian probability; Artificial intelligence; Data mining; Algorithm; Machine learning; Pattern recognition (psychology); Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02904593789911645,"gpt":0.3099755801192388,"spread":0.2809296422201223,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003035869,0.0002742015,0.0004901983,0.0006196664,0.0002773832,0.0002840947,0.0002207299,0.0001049371,0.0006681058],"category_scores_gemma":[0.00003259336,0.0002015124,0.0003389135,0.002275006,0.00004519715,0.0001497335,0.000042293,0.0002113928,0.00008607637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009754137,"about_ca_system_score_gemma":0.00007034836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006914481,"about_ca_topic_score_gemma":0.0000566796,"domain_scores_codex":[0.9979995,0.00008222398,0.0005184429,0.0006065045,0.0004703182,0.0003229776],"domain_scores_gemma":[0.9987627,0.0001406315,0.0001194671,0.0006371164,0.0001519299,0.000188192],"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":[0.0006051704,0.002807615,0.00772185,0.001654039,0.01385063,0.0007222532,0.006696316,0.4622935,0.01167758,0.4474379,0.02100608,0.02352708],"study_design_scores_gemma":[0.0002748604,0.0000241928,0.00005174367,0.0002030109,0.00160103,0.00001353739,0.000137901,0.9872292,0.000342663,0.009411472,0.0004157138,0.0002946694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02580673,0.0001811746,0.9670696,0.001452228,0.00003312591,0.0002891256,0.00002636569,0.0005551644,0.004586482],"genre_scores_gemma":[0.9018617,0.00001849367,0.09606945,0.0001036347,0.00006400056,0.00005580228,0.00008374374,0.00005482285,0.00168832],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.876055,"threshold_uncertainty_score":0.8217438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4390740418","doi":"10.1214/23-ba1411","title":"Large Sample Asymptotic Analysis for Normalized Random Measures with Independent Increments","year":2024,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Mathematics; Estimator; Nonparametric statistics; Consistency (knowledge bases); Applied mathematics; von Mises yield criterion; Prior probability; Bayesian probability; Statistics; Discrete mathematics; Finite element method","retraction":null,"screen_n_in":null,"score":{"opus":0.01599345813547863,"gpt":0.277231280866869,"spread":0.2612378227313904,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00225436,0.0003765828,0.001003693,0.001966072,0.0002658131,0.0007810093,0.0009326836,0.0001353453,0.0001547998],"category_scores_gemma":[0.0000986064,0.0002819863,0.001272734,0.007692382,0.00003730709,0.0005110938,0.0001557929,0.0001924087,0.00001190634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040766,"about_ca_system_score_gemma":0.0001471343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002374025,"about_ca_topic_score_gemma":0.001234588,"domain_scores_codex":[0.996486,0.0003627221,0.0005696834,0.001089658,0.0008215068,0.0006704217],"domain_scores_gemma":[0.9978001,0.000482477,0.0001476,0.001092229,0.0002038426,0.0002737708],"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":[0.0009996321,0.00109481,0.07779795,0.0005132722,0.2288588,0.0003535517,0.008386542,0.01151829,0.0007860385,0.4871347,0.002280862,0.1802756],"study_design_scores_gemma":[0.002144444,0.0001085181,0.003622178,0.00002889902,0.01912388,0.00000538592,0.00003616275,0.9555115,0.0004238249,0.01331139,0.005071566,0.0006123237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007259656,0.0008765985,0.996442,0.0004280931,0.0001087877,0.0004231171,0.00009595486,0.0002749879,0.0006245474],"genre_scores_gemma":[0.5954149,0.0000344104,0.4037366,0.0002191151,0.00005674176,0.00009543999,0.00007430946,0.0000205103,0.0003479658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9439932,"threshold_uncertainty_score":0.9999632,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2946686544","doi":"10.1214/20-ba1250","title":"Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods","year":2020,"lang":"en","type":"preprint","venue":"Bayesian Analysis","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Approximate Bayesian computation; Markov chain Monte Carlo; Computer science; Computation; Bayesian probability; Scope (computer science); Limiting; Sampling (signal processing); Algorithm; Machine learning; Artificial intelligence; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1261288520529594,"gpt":0.4261759453315928,"spread":0.3000470932786334,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01100457,0.001086125,0.00286538,0.001261997,0.0003086663,0.0004923893,0.002073918,0.0008102808,0.00003769183],"category_scores_gemma":[0.00247931,0.0008316084,0.002720506,0.002387609,0.00007110099,0.0001032056,0.000931301,0.001524127,2.689231e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002494193,"about_ca_system_score_gemma":0.0001668488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001234266,"about_ca_topic_score_gemma":0.0002997318,"domain_scores_codex":[0.9915332,0.002970043,0.001810436,0.001829443,0.0007588856,0.001097999],"domain_scores_gemma":[0.9938828,0.001847727,0.001203757,0.002576754,0.0001860789,0.0003028861],"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":[0.0014687,0.00343221,0.006396703,0.03349652,0.04725907,0.0008363671,0.07974158,0.006499433,0.003279634,0.4365778,0.06811116,0.3129008],"study_design_scores_gemma":[0.001089412,0.0000813165,0.0001020746,0.0003123142,0.01107233,0.00001129055,0.007164007,0.7185512,0.0006644511,0.2569784,0.002261062,0.001712092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004523181,0.0003050885,0.985082,0.0040801,0.0003532659,0.00240072,0.0002887525,0.0002098859,0.006827895],"genre_scores_gemma":[0.1031787,0.00009134862,0.8931945,0.00054079,0.0004783131,0.001271859,0.0003648277,0.000183865,0.0006957842],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7120518,"threshold_uncertainty_score":0.9994135,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7083291342","doi":"10.1214/25-ba1552","title":"Bayesian Time-Varying Tensor Vector Autoregressive Models for Dynamic Effective Connectivity","year":2025,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Università Bocconi","keywords":"Tensor (intrinsic definition); Autoregressive model; Rank (graph theory); Bayesian probability; Prior probability; Model selection; Ising model; Dynamic Bayesian network","retraction":null,"screen_n_in":null,"score":{"opus":0.005864160067632578,"gpt":0.2405208010678671,"spread":0.2346566410002345,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004543918,0.0003057265,0.0006406594,0.0004555685,0.0003831352,0.0001738051,0.000825274,0.0001773123,0.00005684796],"category_scores_gemma":[0.0003890612,0.000286573,0.0005754918,0.001823066,0.00007692131,0.0003416372,0.0002327181,0.0001804541,0.00001287972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630082,"about_ca_system_score_gemma":0.0001080261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005423967,"about_ca_topic_score_gemma":0.00002503258,"domain_scores_codex":[0.9978464,0.0001708144,0.0003277793,0.0009494287,0.0002029559,0.0005026151],"domain_scores_gemma":[0.9977821,0.0007286487,0.0002018006,0.0008818943,0.0002827419,0.0001228027],"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":[0.0003457107,0.001050078,0.008648929,0.0009611637,0.01813972,0.0002042391,0.003606772,0.6922454,0.01830073,0.1323559,0.005995939,0.1181454],"study_design_scores_gemma":[0.00039665,0.00003549859,0.00193896,0.00004201075,0.0005700766,0.000002632894,0.0000240953,0.9616833,0.001926369,0.03277766,0.0003103387,0.0002924227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001281077,0.0001212717,0.9825276,0.002878732,0.0001091972,0.000513197,0.00002082417,0.0002603716,0.01228774],"genre_scores_gemma":[0.9692636,0.000002780053,0.02368854,0.0002401597,0.00003281746,0.0002023067,0.00003213744,0.000005514563,0.006532194],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9679825,"threshold_uncertainty_score":0.9999586,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4384572568","doi":"10.1214/23-ba1397","title":"Defining a Credible Interval Is Not Always Possible with “Point-Null” Priors: A Lesser-Known Correlate of the Jeffreys-Lindley Paradox (with Discussion)","year":2023,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","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 British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Science Foundation","keywords":"Prior probability; Mathematics; Interval (graph theory); Mathematical economics; Point estimation; Applied mathematics; Null (SQL); Bayesian probability; Econometrics; Statistics; Computer science; Combinatorics; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.04654015112076346,"gpt":0.3352757282936082,"spread":0.2887355771728448,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002253932,0.0003933203,0.001113406,0.001347417,0.0004346589,0.0005717473,0.001750141,0.0001695121,0.0009220256],"category_scores_gemma":[0.0005213988,0.0001569414,0.00082534,0.00903379,0.0003271876,0.0004966872,0.000490204,0.000378357,0.0006428135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007214402,"about_ca_system_score_gemma":0.0002694772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002667191,"about_ca_topic_score_gemma":0.001858904,"domain_scores_codex":[0.9950728,0.0002528177,0.001331925,0.001093875,0.001681067,0.0005675256],"domain_scores_gemma":[0.9955835,0.0008235438,0.001011146,0.00197368,0.0003633935,0.000244706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001056391,0.000452249,0.6682098,0.0000234429,0.001605812,0.0001567003,0.007932261,0.01717455,0.0001536192,0.0009403194,0.01102678,0.2912681],"study_design_scores_gemma":[0.004984267,0.001671772,0.6166052,0.001635944,0.008430952,0.0001496768,0.02786587,0.2889699,0.004106325,0.03239815,0.01038412,0.002797845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975388,0.00003400863,0.01826692,0.003878966,0.0002328382,0.0002617764,0.0001263181,0.0001123269,0.001698875],"genre_scores_gemma":[0.9904786,0.00001406404,0.003295965,0.0002315768,0.00003272234,0.00002158123,0.0000152544,0.00004147333,0.005868701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2884702,"threshold_uncertainty_score":0.9999912,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406101628","doi":"10.1214/24-ba1503","title":"A Differential Geometric Approach to Bayesian Marginalization","year":2025,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Bayesian probability; Mathematics; Differential (mechanical device); Computer science; Applied mathematics; Artificial intelligence; Econometrics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01010176193853096,"gpt":0.2612706187798262,"spread":0.2511688568412952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004148151,0.0003019066,0.0006034567,0.003810667,0.0002080037,0.0004269292,0.001308614,0.0001491896,0.00009017552],"category_scores_gemma":[0.00008171613,0.0002791147,0.000463033,0.01801127,0.00002984015,0.0002732213,0.0003200978,0.0001648609,0.00002117657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000185,"about_ca_system_score_gemma":0.00009664561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009406351,"about_ca_topic_score_gemma":0.00002312035,"domain_scores_codex":[0.9973456,0.0002741201,0.0004670288,0.0009805494,0.0004301301,0.0005025616],"domain_scores_gemma":[0.998133,0.00007316248,0.0001139958,0.001273041,0.0001385327,0.0002682747],"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":[0.00002410305,0.0005144908,0.004782004,0.0000797515,0.00191252,0.00001238322,0.000562666,0.001388146,0.0003050693,0.7068942,0.008553236,0.2749715],"study_design_scores_gemma":[0.0004893732,0.00004804993,0.01840932,0.00002252554,0.00133614,0.000004146397,0.00002476374,0.9503008,0.0007955927,0.02492631,0.002999845,0.0006431699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001891859,0.0001198925,0.9663638,0.001093703,0.000167914,0.0002675137,0.000004420646,0.000200264,0.03159332],"genre_scores_gemma":[0.4747008,0.000009521769,0.5210486,0.0006605646,0.00004627344,0.00004151387,0.00001878696,0.00001005943,0.003463859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9489126,"threshold_uncertainty_score":0.9999661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}