{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":26,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":26,"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":"e7642a1bc9c1","filters":{"venue":"Lecture notes in statistics"}},"results":[{"id":"W634979280","doi":"10.1007/0-387-35439-5","title":"Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series","year":2006,"lang":"en","type":"book","venue":"Lecture notes in statistics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; Series (stratigraphy); Distribution (mathematics); Computer science; Geography; Mathematics; Economics; Geology; Management; Paleontology","retraction":null,"screen_n_in":null,"score":{"opus":0.01347607562010095,"gpt":0.2841560288380697,"spread":0.2706799532179688,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005521653,0.0002946793,0.0004899327,0.00009626093,0.0001565312,0.0002201189,0.0002667417,0.0002907982,0.00002701615],"category_scores_gemma":[0.0005688302,0.0002848843,0.00006992771,0.0001695749,0.00009238388,0.0001584672,0.0001264038,0.0002585851,0.000002682534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001965974,"about_ca_system_score_gemma":0.0001914183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005504872,"about_ca_topic_score_gemma":0.0002887022,"domain_scores_codex":[0.9984672,0.00008576876,0.0005013013,0.0005069962,0.0001560552,0.0002826939],"domain_scores_gemma":[0.9979632,0.001070921,0.0003914548,0.0003092358,0.000216889,0.00004830757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001722434,0.00001588571,0.0002492667,0.0002128669,0.00006296913,0.000009496614,0.0002621252,0.001205296,0.00000757435,0.05337188,0.04195563,0.9026298],"study_design_scores_gemma":[0.0001862738,0.0001722347,0.0001980121,0.00008932868,0.00006992966,0.00001266694,5.775138e-7,0.287089,0.00003414498,0.3156336,0.3960799,0.0004343126],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00000153889,0.000489909,0.995911,0.0001297477,0.0002118168,0.0002549005,0.001160801,0.00004738276,0.001792905],"genre_scores_gemma":[0.00005356826,0.00002605331,0.9733472,0.00006300061,0.0002795756,0.00001917307,0.004443267,0.00002408178,0.02174409],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9021955,"threshold_uncertainty_score":0.9999603,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2222558555","doi":"10.1007/978-3-642-35407-6_5","title":"Assessing and Modeling Asymmetry in Bivariate Continuous Data","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Bivariate analysis; Asymmetry; Econometrics; Computer science; Statistics; Mathematics; Physics; Particle physics","retraction":null,"screen_n_in":null,"score":{"opus":0.2183110533761694,"gpt":0.4111082095713619,"spread":0.1927971561951925,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007106909,0.0004778162,0.000957219,0.0002372311,0.00005182116,0.0002524726,0.0004023502,0.0005517955,0.0004577695],"category_scores_gemma":[0.01011212,0.0004372492,0.00002314955,0.00006475347,0.0001320981,0.0001050479,0.0004168995,0.001209526,0.00002196986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008298003,"about_ca_system_score_gemma":0.00009583242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002290037,"about_ca_topic_score_gemma":0.0002075369,"domain_scores_codex":[0.9975659,0.0001142875,0.0008680529,0.0007236039,0.0003267939,0.0004013869],"domain_scores_gemma":[0.9907703,0.007883643,0.0002894963,0.0008275969,0.0001281097,0.0001008671],"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":[0.000008622784,0.00002455382,0.00005733651,0.0003624683,0.00003085587,0.0001108031,0.0001212981,0.0001001297,0.000007344806,0.6938872,0.0002884928,0.3050009],"study_design_scores_gemma":[0.0002504959,0.00002929247,0.00003866421,0.0006185051,0.00006895083,0.00001033522,0.000005554058,0.2288742,0.000002277195,0.7692063,0.0005117154,0.0003837237],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006438776,0.0005627166,0.9749053,0.00007010031,0.0002258904,0.0003541302,0.00118288,0.00003528492,0.02259938],"genre_scores_gemma":[0.01313106,0.0002213987,0.9852377,0.0001775477,0.0001358166,0.000007699907,0.0002083552,0.0001118702,0.0007685061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3046171,"threshold_uncertainty_score":0.999808,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1566366787","doi":"10.1007/978-1-4613-0141-7_12","title":"Ancillary Information for Statistical Inference","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Scalar (mathematics); Mathematics; Inference; Nuisance parameter; Dimension (graph theory); Affine transformation; Applied mathematics; Variable (mathematics); Order (exchange); Third order; Tangent; Computer science; Statistics; Estimator; Mathematical analysis; Pure mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.07038244963229102,"gpt":0.3723573659195813,"spread":0.3019749162872903,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002173466,0.0004438136,0.0005731366,0.0001888491,0.0001319237,0.00009336486,0.000218731,0.0005018076,0.001717991],"category_scores_gemma":[0.008655442,0.0004519134,0.00006978781,0.00009281074,0.0001998668,0.0001089207,0.00005244989,0.0005351243,0.0002098399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002672431,"about_ca_system_score_gemma":0.0002324426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001027882,"about_ca_topic_score_gemma":0.0001062051,"domain_scores_codex":[0.9978117,0.0000241237,0.001005087,0.0003327893,0.0004427934,0.0003835246],"domain_scores_gemma":[0.9907348,0.007778002,0.0004129352,0.0004131598,0.00050811,0.0001530137],"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":[0.0000286233,0.00003078409,0.000007359147,0.0002828787,0.00002299175,0.000007142914,0.00006096433,0.0001036516,8.744902e-7,0.9122262,0.01368799,0.07354052],"study_design_scores_gemma":[0.000429616,0.00006544164,0.00007569918,0.0001081655,0.00009205312,0.000008556101,0.000002135494,0.01128271,0.000006191226,0.8359337,0.1515895,0.0004061463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[9.273705e-7,0.00003910988,0.9221521,0.0002216203,0.0001500147,0.0009881423,0.03152089,0.0001084351,0.04481876],"genre_scores_gemma":[0.006459856,0.0001716496,0.9696377,0.0009414222,0.0002337534,0.0003086827,0.01767702,0.0001131394,0.004456806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1379015,"threshold_uncertainty_score":0.9997932,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W952142941","doi":"10.1007/978-1-4613-0141-7_13","title":"The Relevance Weighted Likelihood With Applications","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of Regina","funders":"","keywords":"Nonparametric statistics; Weighting; Relevance (law); Extension (predicate logic); Variance (accounting); Econometrics; Parametric statistics; Computer science; Nonparametric regression; Statistics; Mathematics; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.04150135593609128,"gpt":0.3550000045259813,"spread":0.3134986485898901,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002515395,0.0005029708,0.0005814308,0.00007232317,0.0002565902,0.00005536835,0.0003371852,0.0003433363,0.0001107762],"category_scores_gemma":[0.001173384,0.0003259777,0.00005345813,0.00009801814,0.0003447289,0.000028184,0.00006632838,0.001068635,0.00002875139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001436916,"about_ca_system_score_gemma":0.0001282684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005865542,"about_ca_topic_score_gemma":0.0006974956,"domain_scores_codex":[0.9978677,0.00005474348,0.0006022818,0.0005379203,0.0004463751,0.0004909788],"domain_scores_gemma":[0.9852308,0.01319503,0.0003858558,0.0008076259,0.0002615713,0.0001191296],"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":[0.00003759272,0.00001781135,0.000001426725,0.00007276273,0.00003596873,0.00004267036,0.00004104459,0.0000406738,0.000001545322,0.7014711,0.0003821536,0.2978552],"study_design_scores_gemma":[0.000213212,0.00007954994,0.000001170093,0.0001492074,0.00009878942,0.00001933094,0.000001604943,0.0008578164,0.00001079222,0.7557545,0.2424802,0.0003338189],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[2.272943e-7,0.000910055,0.9301968,0.000199318,0.00008756622,0.0008770857,0.0009486694,0.00007536251,0.06670493],"genre_scores_gemma":[0.00009195067,0.001710818,0.9795873,0.000180238,0.0002332764,0.0001726924,0.00009828539,0.0001713978,0.01775404],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2975214,"threshold_uncertainty_score":0.9999192,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W51237428","doi":"10.1007/978-1-4613-0141-7_6","title":"Bayes and Empirical Bayes Estimates of Survival and Hazard Functions of a Class of Distributions","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Bayes' theorem; Estimator; Statistics; Mathematics; Class (philosophy); Hazard ratio; Point estimation; Monte Carlo method; Hazard; Econometrics; Applied mathematics; Computer science; Bayesian probability; Artificial intelligence; Confidence interval; Chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.06551921107562778,"gpt":0.3605012284162806,"spread":0.2949820173406529,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001598978,0.000303411,0.0008286645,0.0001539346,0.00006875478,0.00001415731,0.0001040794,0.000313997,0.0003864735],"category_scores_gemma":[0.00306483,0.0002893734,0.00006529333,0.0001364102,0.000840139,0.00002715443,0.00006426044,0.0003169986,0.000002268482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004519913,"about_ca_system_score_gemma":0.0001022443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002120857,"about_ca_topic_score_gemma":0.0002108629,"domain_scores_codex":[0.998221,0.00003588691,0.000944656,0.0003083226,0.0003080394,0.000182026],"domain_scores_gemma":[0.9926199,0.00602983,0.0005384707,0.0003026944,0.0004104388,0.00009870048],"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":[0.00002811958,0.0001161116,0.001114596,0.0006280637,0.00008092921,0.000003197435,0.0001118758,0.00003237241,0.00005795277,0.9890097,0.0009033075,0.007913736],"study_design_scores_gemma":[0.000427576,0.0001247202,0.004249007,0.0003095269,0.0003614652,0.00001493253,0.00001745527,0.005664639,0.0002012905,0.98607,0.002295267,0.0002640867],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003974488,0.0002476157,0.9624952,0.000206553,0.00005138257,0.0003133718,0.03054032,0.00002500217,0.005723073],"genre_scores_gemma":[0.5377789,0.0007360852,0.4560294,0.0000386088,0.00007598246,0.00005704008,0.00391191,0.0001080128,0.001264108],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5373814,"threshold_uncertainty_score":0.9999558,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W968624666","doi":"10.1007/978-1-4614-6871-4_8","title":"Response-Dependent Sampling with Clustered and Longitudinal Data","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Sampling (signal processing); Context (archaeology); Inference; Sampling design; Statistics; Parametric statistics; Computer science; Clinical study design; Sample size determination; Longitudinal data; Econometrics; Data mining; Medicine; Artificial intelligence; Mathematics; Clinical trial; Environmental health; Population; Geography; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.1514000731768205,"gpt":0.3863289443927883,"spread":0.2349288712159677,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006881548,0.000548966,0.0008156754,0.0001611588,0.00009233491,0.0001588678,0.0004610863,0.0004194547,0.0009112353],"category_scores_gemma":[0.005225211,0.0004311152,0.00002100141,0.00004507486,0.0002986341,0.00006716958,0.0004085815,0.0009247616,0.00003228866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009185049,"about_ca_system_score_gemma":0.0001546985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004786651,"about_ca_topic_score_gemma":0.0005146246,"domain_scores_codex":[0.9975181,0.0001417272,0.0006121407,0.0008448882,0.0004878211,0.0003952996],"domain_scores_gemma":[0.9842623,0.01395311,0.0003343587,0.001141006,0.0001633967,0.0001458071],"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":[0.0005907153,0.00003450364,0.0001658009,0.0005284811,0.0001295454,0.0003434836,0.0002050987,0.000007570533,0.00001561596,0.8011026,0.000979815,0.1958968],"study_design_scores_gemma":[0.0004773741,0.0002424787,0.0003898377,0.0006763287,0.0002016825,0.0001081346,0.000003619683,0.001822026,0.000009489489,0.993014,0.002471343,0.0005837343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002343287,0.0002394423,0.9908692,0.000156395,0.0001303187,0.0005065915,0.003874067,0.00005014198,0.004150362],"genre_scores_gemma":[0.001397438,0.00009410195,0.9953043,0.0001310702,0.0001193902,0.00001265459,0.0001931546,0.0001308402,0.002617031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.195313,"threshold_uncertainty_score":0.9998141,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4238614988","doi":"10.1007/978-1-4613-0111-0_4","title":"Compound distributions","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Portfolio; Aggregate (composite); Random variable; Econometrics; Probabilistic logic; Heavy-tailed distribution; Distribution (mathematics); Ruin theory; Mathematics; Actuarial science; Joint probability distribution; Risk model; Statistical physics; Economics; Statistics; Financial economics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.1149217694336418,"gpt":0.3760107584306925,"spread":0.2610889889970506,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008783706,0.00036886,0.0006621444,0.0002538609,0.0001793803,0.0002070458,0.0007668206,0.0005613723,0.002309316],"category_scores_gemma":[0.003423877,0.0002844382,0.0001311532,0.0001797174,0.0004543891,0.00007008426,0.0001339539,0.0009359618,0.0006448081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000210311,"about_ca_system_score_gemma":0.0001870128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004885834,"about_ca_topic_score_gemma":0.002187971,"domain_scores_codex":[0.9966732,0.00008323311,0.0009707019,0.0007203429,0.001199735,0.0003528387],"domain_scores_gemma":[0.9922623,0.005908975,0.0003430415,0.0009611438,0.0003981983,0.0001263538],"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":[0.0000285932,0.00004802351,0.0002694426,0.00001577509,0.00002556059,0.0002111439,0.0002369606,0.004899014,0.000002019587,0.8319902,0.01045707,0.1518161],"study_design_scores_gemma":[0.000111593,0.00003005001,0.00007756845,0.00003582642,0.00002013746,0.00001944144,8.882293e-7,0.001972876,0.000003939919,0.7010756,0.2964204,0.000231724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001408242,0.0005841188,0.8756438,0.0004886888,0.0004987461,0.0002430993,0.005563044,0.00003414418,0.1169303],"genre_scores_gemma":[0.2421177,0.003532433,0.3818946,0.002893173,0.002247369,0.0000615431,0.005296205,0.0003574704,0.3615995],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4937492,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W56489066","doi":"10.1007/978-1-4613-0141-7_9","title":"Bayesian and Likelihood Inference for the Generalized Fieller—Creasy Problem","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of Regina","funders":"","keywords":"Frequentist inference; Prior probability; Markov chain Monte Carlo; Mathematics; Bayesian inference; Bayesian probability; Inference; Bayes factor; Matching (statistics); Bayesian statistics; Applied mathematics; Algorithm; Computer science; Statistics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.04676580047843084,"gpt":0.3477503579115554,"spread":0.3009845574331246,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004728324,0.0005918487,0.000825617,0.0001149875,0.0001831902,0.0001321358,0.0003363368,0.0005546624,0.0007948963],"category_scores_gemma":[0.004565144,0.0004181333,0.00009190891,0.00006923542,0.0002839213,0.00002877955,0.0001197977,0.0007875578,0.000006023913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006820903,"about_ca_system_score_gemma":0.0001590582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008621963,"about_ca_topic_score_gemma":0.0009803473,"domain_scores_codex":[0.9977421,0.00008567156,0.0007419419,0.0005763393,0.0003327898,0.0005212193],"domain_scores_gemma":[0.9768339,0.02193536,0.0003437707,0.0005366984,0.000211767,0.0001384633],"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":[0.00004294956,0.0000179403,0.0000314961,0.0002999523,0.00006059895,0.00002482933,0.0001801313,0.000006352393,0.000003185821,0.7106372,0.001163113,0.2875322],"study_design_scores_gemma":[0.0005602552,0.0001637141,0.00002865371,0.0003389846,0.0002571849,0.00002174003,0.000003263262,0.007530179,0.00001279444,0.9692753,0.02129275,0.0005151898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[8.439301e-7,0.0007170246,0.9744427,0.0004009085,0.0001980987,0.001227464,0.001536669,0.00005001242,0.02142631],"genre_scores_gemma":[0.0004733476,0.00139008,0.9935434,0.0004523882,0.0003147571,0.0001240958,0.00008582101,0.0001189985,0.003497141],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.287017,"threshold_uncertainty_score":0.999827,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2498801788","doi":"10.1007/978-3-319-31260-6_8","title":"Penalized Generalized Quasi-Likelihood Based Variable Selection for Longitudinal Data","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Feature selection; Computer science; Likelihood function; Covariate; Penalty method; Inference; Model selection; Biometrics; Variable (mathematics); Selection (genetic algorithm); Function (biology); Variance (accounting); Algorithm; Mathematics; Mathematical optimization; Data mining; Artificial intelligence; Machine learning; Estimation theory","retraction":null,"screen_n_in":null,"score":{"opus":0.153394232988414,"gpt":0.3865815205119637,"spread":0.2331872875235497,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001085349,0.0006916606,0.001193462,0.0002163529,0.0001452604,0.00009895415,0.0006607263,0.0006907684,0.003066778],"category_scores_gemma":[0.01396697,0.0005579784,0.00009507693,0.00009482571,0.0001552941,0.00006332376,0.0001691942,0.0006127877,0.00002858016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002300841,"about_ca_system_score_gemma":0.0005379793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005729669,"about_ca_topic_score_gemma":0.0004192018,"domain_scores_codex":[0.9965824,0.0001587377,0.001009004,0.001071101,0.0005221049,0.0006566162],"domain_scores_gemma":[0.9826074,0.01504439,0.0005583902,0.001140016,0.000488742,0.0001611143],"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":[0.0002601584,0.00008244099,0.00003654488,0.0006144479,0.0001178804,0.00001306671,0.00001774027,0.00001596522,0.00007750436,0.9341831,0.005903496,0.05867763],"study_design_scores_gemma":[0.00170981,0.0002954952,0.000007160212,0.0005253069,0.0004116186,0.000009955107,2.923489e-7,0.03984636,0.00008972231,0.9274438,0.02897596,0.0006845441],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001022628,0.0001588789,0.9707235,0.0001383122,0.000539923,0.001001541,0.01791221,0.00009480212,0.009429843],"genre_scores_gemma":[0.0001454939,0.00004679541,0.9912458,0.0002820684,0.0005681577,0.0000812061,0.00127494,0.0001951981,0.006160342],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05799309,"threshold_uncertainty_score":0.9996872,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2478895115","doi":"10.1007/978-3-319-31260-6_3","title":"Zero-Inflated Spatial Models: Application and Interpretation","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; Simon Fraser University","funders":"","keywords":"Overdispersion; Statistics; Count data; Mathematics; Multivariate statistics; Econometrics; Generalized linear model; Computer science; Poisson distribution","retraction":null,"screen_n_in":null,"score":{"opus":0.03482270418686196,"gpt":0.3273210291152349,"spread":0.292498324928373,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002331212,0.000414947,0.000591746,0.0001594397,0.00005328539,0.00004997527,0.0001507166,0.0005185857,0.0001804935],"category_scores_gemma":[0.00159323,0.0003382724,0.00004075343,0.00003529334,0.0001921543,0.00005406751,0.00007641016,0.0004863639,0.00002426748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001165518,"about_ca_system_score_gemma":0.00006507836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003086222,"about_ca_topic_score_gemma":0.0002024901,"domain_scores_codex":[0.9982488,0.00006653088,0.0006348368,0.0005035605,0.0002914479,0.0002548001],"domain_scores_gemma":[0.9944722,0.00453572,0.0003590908,0.0003644818,0.000175994,0.0000924839],"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":[0.00003417839,0.000006688972,0.000004900539,0.0001222233,0.00001901852,0.000009343263,0.0001229831,0.000007029016,0.00002192309,0.5594203,0.00005663521,0.4401747],"study_design_scores_gemma":[0.00028019,0.00007644842,0.00001480574,0.0004246406,0.00009721291,0.0000086259,5.095416e-7,0.0625298,0.00003729467,0.9356317,0.000520286,0.0003784661],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000002308578,0.000130541,0.9703506,0.00007449857,0.0001445919,0.0004874638,0.001139369,0.0000644454,0.02760614],"genre_scores_gemma":[0.02571702,0.0001748543,0.9722171,0.000122632,0.0001394557,0.00003633248,0.0001095792,0.0001049003,0.001378115],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4397963,"threshold_uncertainty_score":0.999907,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W34298671","doi":"10.1007/978-1-4614-3520-4_35","title":"Statistical Analyses of Data Cubes","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Dendrogram; Object (grammar); Position (finance); Data cube; Computer science; Domain (mathematical analysis); Statistical physics; Pattern recognition (psychology); Data mining; Artificial intelligence; Mathematics; Physics; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.1274519497233187,"gpt":0.4091473613771846,"spread":0.2816954116538659,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002057424,0.0002579497,0.0004668663,0.0001410117,0.00004442928,0.00006367851,0.0005153719,0.0001001197,0.007010105],"category_scores_gemma":[0.0001875217,0.0002168102,0.00004265829,0.00005796277,0.0003972378,0.0001215259,0.0003134818,0.0003902635,0.00006938793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001986187,"about_ca_system_score_gemma":0.0002133688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003767941,"about_ca_topic_score_gemma":0.0001201627,"domain_scores_codex":[0.9983411,0.00003214728,0.0004160413,0.0003920844,0.0004793997,0.0003392705],"domain_scores_gemma":[0.9975566,0.0011928,0.0001783484,0.0008495709,0.0001044803,0.0001181666],"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":[0.00004916408,0.00008820356,0.003404922,0.0001538894,0.0002381237,0.00001741735,0.0001479245,0.000174061,0.0001077123,0.7300553,0.01175676,0.2538066],"study_design_scores_gemma":[0.00034302,0.00006764443,0.0005689082,0.0001345735,0.0002316307,0.000001030655,0.00001725674,0.001672531,0.0009394622,0.9500828,0.04543092,0.0005101993],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00002245455,0.0009201076,0.8909465,0.00001653356,0.0001635685,0.0001507225,0.04353495,0.00000697203,0.06423817],"genre_scores_gemma":[0.574877,0.0002418229,0.3524023,0.00006932125,0.001635688,0.00001556539,0.04442648,0.0001757598,0.02615602],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5748546,"threshold_uncertainty_score":0.9938976,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W566021556","doi":"10.1007/978-1-4613-0147-9_11","title":"Some Statistical Aspects of Magnetoencephalography","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Magnetoencephalography; Inverse problem; Computer science; Statistical physics; Nonlinear system; Simplicity; Noise (video); Algorithm; Dipole; Mathematics; Artificial intelligence; Physics; Mathematical analysis; Psychology; Electroencephalography; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03151330268191886,"gpt":0.2728391427682101,"spread":0.2413258400862912,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001292876,0.0004979581,0.000737321,0.0004352446,0.00009247715,0.0000271823,0.0002984959,0.0003336285,0.0007320339],"category_scores_gemma":[0.01251461,0.0004941839,0.00009669302,0.0001686642,0.0008852766,0.00006472442,0.0001440702,0.0008368776,0.00006852639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122952,"about_ca_system_score_gemma":0.0001407355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003138931,"about_ca_topic_score_gemma":0.0002746468,"domain_scores_codex":[0.9973776,0.00007663336,0.0005606926,0.0008390002,0.0007287137,0.0004173689],"domain_scores_gemma":[0.9776924,0.02137947,0.0003033865,0.0004264818,0.0001128702,0.00008534588],"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":[0.00006897961,0.000057925,0.0001103916,0.0001479341,0.00002768098,0.0005615336,0.00007077578,0.0003335609,0.0006119926,0.9819695,0.002629127,0.01341065],"study_design_scores_gemma":[0.0002664538,0.0003711756,0.000353591,0.0001117713,0.0000573304,0.00004456179,9.944137e-7,0.0001969799,0.001210995,0.9676633,0.02927169,0.0004511349],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00009010242,0.002589268,0.5433222,0.001344548,0.002434061,0.001157686,0.01332368,0.0001953073,0.4355431],"genre_scores_gemma":[0.7428802,0.01174306,0.1756453,0.02395451,0.004714196,0.000180405,0.001052998,0.00114663,0.0386827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.74279,"threshold_uncertainty_score":0.999751,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W341900282","doi":"10.1007/978-1-4613-0111-0_3","title":"Mixed Poisson distributions","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Poisson distribution; Compound Poisson distribution; Mathematics; Zero-inflated model; Poisson binomial distribution; Distribution (mathematics); Statistics; Applied mathematics; Statistical physics; Compound Poisson process; Poisson regression; Mathematical analysis; Poisson process; Population; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.1295048111341935,"gpt":0.3655369418093195,"spread":0.236032130675126,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001049869,0.0004137371,0.0007082804,0.0002955901,0.0001739511,0.0001861402,0.0008003735,0.0007025053,0.002094119],"category_scores_gemma":[0.005948945,0.0003183746,0.0001556659,0.0002184948,0.0003571057,0.0000769137,0.0001390239,0.0009829756,0.000634267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002274053,"about_ca_system_score_gemma":0.0001936976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006232981,"about_ca_topic_score_gemma":0.002882916,"domain_scores_codex":[0.9963323,0.0001069888,0.001037121,0.0008177856,0.001295874,0.0004099356],"domain_scores_gemma":[0.9926621,0.005321599,0.000389783,0.001045711,0.0004334313,0.000147415],"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":[0.0000325108,0.00004421216,0.0001977096,0.00001519017,0.00002330594,0.000136446,0.0002236071,0.003182251,0.000003418433,0.730474,0.01068059,0.2549868],"study_design_scores_gemma":[0.0001356925,0.00004173249,0.0001532004,0.00004615857,0.00002701272,0.00001376658,0.00000141415,0.001959139,0.00001508975,0.7581121,0.2392135,0.0002811374],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002809212,0.0006533068,0.925795,0.0007472524,0.0007197955,0.0002834915,0.006140487,0.00004382067,0.06558873],"genre_scores_gemma":[0.2342434,0.003477294,0.4098346,0.002088795,0.002103543,0.00007472392,0.005082745,0.0003767962,0.3427181],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5159605,"threshold_uncertainty_score":0.9999268,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2505248766","doi":"10.1007/978-3-319-31260-6_6","title":"Dynamic Models for Longitudinal Ordinal Non-stationary Categorical Data","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Categorical variable; Ordinal data; Ordinal regression; Longitudinal data; Econometrics; Computer science; Mathematics; Statistics; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.1213200627463977,"gpt":0.3954614555441705,"spread":0.2741413927977728,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004966039,0.0006585954,0.001008866,0.0002131377,0.000122579,0.00006868091,0.0008085007,0.000610028,0.0005066126],"category_scores_gemma":[0.003581204,0.0005280813,0.00008982095,0.00006298366,0.0002744861,0.0001208887,0.0003108949,0.0007031283,0.00003415666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002647829,"about_ca_system_score_gemma":0.0003501734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001430421,"about_ca_topic_score_gemma":0.0002219915,"domain_scores_codex":[0.9968351,0.00005676369,0.0009361553,0.001069208,0.0005315799,0.0005712282],"domain_scores_gemma":[0.9838554,0.01401859,0.0004209217,0.001208465,0.0003380734,0.0001586045],"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":[0.00009898394,0.00003983626,0.000007849369,0.0004422991,0.00007745491,0.000125968,0.00004619978,0.00002859995,0.000003554595,0.8326023,0.00282233,0.1637047],"study_design_scores_gemma":[0.0005116538,0.0001607248,0.00002170211,0.0002985875,0.0002316293,0.0000362764,0.000001059638,0.1060624,0.000002323996,0.8902059,0.001851035,0.0006166646],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[3.540652e-7,0.0001646836,0.9607505,0.0002252101,0.0004450981,0.0007835824,0.025681,0.00005147355,0.01189806],"genre_scores_gemma":[0.001481579,0.0001324536,0.9906735,0.000101015,0.0002698386,0.00005567576,0.001666877,0.000175913,0.005443144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.163088,"threshold_uncertainty_score":0.9997171,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W77330898","doi":"10.1007/978-1-4614-6871-4_6","title":"Consistent Estimation in Incomplete Longitudinal Binary Models","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland; Carleton University","funders":"","keywords":"Estimation; Binary number; Econometrics; Computer science; Statistics; Mathematics; Economics; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.1594795387666528,"gpt":0.361704570262022,"spread":0.2022250314953692,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003626105,0.0005649448,0.000977551,0.0003260664,0.00005425524,0.00006760689,0.0002478512,0.0005283018,0.001386587],"category_scores_gemma":[0.003176043,0.0005256537,0.00007329348,0.00008617868,0.0002416865,0.0000653499,0.0001406686,0.001035954,0.0001016885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002817089,"about_ca_system_score_gemma":0.0001380406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001366484,"about_ca_topic_score_gemma":0.0003424355,"domain_scores_codex":[0.9973719,0.0001095301,0.001047528,0.0005726072,0.0004703717,0.0004280232],"domain_scores_gemma":[0.9916898,0.007092927,0.0003914179,0.0005063133,0.0002063179,0.0001132391],"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":[0.00002504054,0.00004775015,0.00004391911,0.0003974034,0.00003135224,0.0001642234,0.0001657125,0.003427282,0.000005211004,0.927734,0.0007184951,0.06723964],"study_design_scores_gemma":[0.0002874359,0.0001118031,0.0001938593,0.0005719125,0.0000610269,0.00001462719,0.000001433542,0.2288258,0.000005312344,0.7692838,0.0002179342,0.0004250609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005907292,0.0002287697,0.9619148,0.00009246421,0.0002444513,0.0006453141,0.000979609,0.00004741888,0.03578805],"genre_scores_gemma":[0.02130914,0.00009602771,0.9759916,0.0001526522,0.00006802537,0.00004907984,0.0001767465,0.0001055399,0.002051173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2253986,"threshold_uncertainty_score":0.9997195,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W195199240","doi":"10.1007/978-3-642-14104-1_8","title":"Robust regression with infinite moving average errors","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Statistics; Regression; Mathematics; Computer science; Econometrics","retraction":null,"screen_n_in":null,"score":{"opus":0.08750569966658531,"gpt":0.3620935658755106,"spread":0.2745878662089253,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000310787,0.0007404003,0.0009794884,0.0002189169,0.0001446611,0.00005068179,0.0002696326,0.0008620397,0.0005985789],"category_scores_gemma":[0.00340335,0.0005522963,0.00007205544,0.00006720548,0.0002954923,0.00005968766,0.0001294279,0.002510534,0.00001171661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144352,"about_ca_system_score_gemma":0.0001337891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001671633,"about_ca_topic_score_gemma":0.0007896582,"domain_scores_codex":[0.997479,0.00006760497,0.0006942687,0.000689176,0.0005684939,0.0005014703],"domain_scores_gemma":[0.992178,0.006178277,0.0005382788,0.0007222335,0.0002067438,0.0001764313],"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":[0.0001634529,0.00005041632,0.00001937143,0.0006277747,0.00007138032,0.0007933409,0.0005749239,0.005384796,0.0001037532,0.8822418,0.0002417272,0.1097273],"study_design_scores_gemma":[0.0004546666,0.0001539075,0.00000478714,0.001022772,0.000122485,0.00003712963,0.000002275545,0.0067614,0.0001257691,0.9827996,0.007791797,0.0007234112],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001074538,0.0001191864,0.975023,0.00005487357,0.0003104478,0.000471337,0.001220571,0.00009176529,0.02269806],"genre_scores_gemma":[0.0009892313,0.0001073263,0.9858897,0.0002038113,0.0002141114,0.0000180983,0.000159254,0.0002472353,0.0121712],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1090039,"threshold_uncertainty_score":0.9997907,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W57323835","doi":"10.1007/978-1-4613-0049-6_6","title":"Designs in the Presence of Trends","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; TRACE (psycholinguistics); Block (permutation group theory); Class (philosophy); Block size; Optimal design; Block design; Degree (music); Matrix (chemical analysis); Binary number; Term (time); Combinatorics; Mathematical optimization; Computer science; Arithmetic; Statistics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.2325307607797628,"gpt":0.4434055247947282,"spread":0.2108747640149654,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002398603,0.0003650707,0.0007060833,0.0006858322,0.00004041114,0.00009940439,0.001442435,0.000376619,0.003529574],"category_scores_gemma":[0.005345373,0.0002370381,0.0001066209,0.0004740882,0.0004137479,0.00006789788,0.0001330759,0.0007707997,0.00007080145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008212275,"about_ca_system_score_gemma":0.00005803183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004818571,"about_ca_topic_score_gemma":0.0001582555,"domain_scores_codex":[0.9956441,0.0005378757,0.00112598,0.0005992475,0.001787901,0.0003049326],"domain_scores_gemma":[0.982117,0.01623111,0.00049733,0.0009553843,0.0001487657,0.00005039586],"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":[0.0001065454,0.0001232081,0.0003937868,0.00002699651,0.00002597688,0.0003771031,0.005724764,0.005303473,0.0004209401,0.2049278,0.01099327,0.7715762],"study_design_scores_gemma":[0.000374588,0.0003587221,0.0006799657,0.0001183955,0.00002955941,0.00002523632,0.00003415949,0.008118229,0.0007186662,0.9627958,0.02627415,0.000472556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001264187,0.001096036,0.7907212,0.0001458153,0.0003011992,0.000281548,0.0004813213,0.000009955238,0.2069503],"genre_scores_gemma":[0.05998426,0.0001606896,0.8888351,0.0005026163,0.0001486844,0.00002382414,0.00004549347,0.00008281025,0.05021652],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7711036,"threshold_uncertainty_score":0.9973813,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W50856701","doi":"10.1007/978-1-4613-0111-0_5","title":"Bounds based on reliability classifications","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Section (typography); Simple (philosophy); Mathematics; Reliability (semiconductor); Function (biology); Aggregate (composite); Residual; Order (exchange); Distribution (mathematics); Combinatorics; Applied mathematics; Discrete mathematics; Computer science; Algorithm; Mathematical analysis; Physics; Economics; Thermodynamics","retraction":null,"screen_n_in":null,"score":{"opus":0.07748339662381937,"gpt":0.3620999574960659,"spread":0.2846165608722465,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002329507,0.0004461968,0.0005009509,0.000191598,0.000175754,0.00006750404,0.0002605766,0.0005082841,0.003727503],"category_scores_gemma":[0.00527839,0.0004418187,0.0001003406,0.0001529214,0.0003127895,0.00002265345,0.00002890102,0.0008648229,0.0003836504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004527857,"about_ca_system_score_gemma":0.0002284909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009121578,"about_ca_topic_score_gemma":0.00009483626,"domain_scores_codex":[0.9977219,0.00005357335,0.0008000833,0.0005871602,0.0005266521,0.0003105958],"domain_scores_gemma":[0.9919983,0.006236048,0.0003410182,0.00096165,0.0003144979,0.0001485494],"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":[0.00001887106,0.0001501295,0.00001764242,0.00009928954,0.00001052126,0.00001221883,0.00002023669,0.0009129817,0.000001566066,0.9761202,0.01094137,0.01169501],"study_design_scores_gemma":[0.0002888646,0.0000545582,0.0002614192,0.0001293137,0.00007865356,0.000002712522,8.365349e-7,0.04087234,0.000007902257,0.8668988,0.09102871,0.0003759183],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001374749,0.00001367097,0.7959961,0.001060915,0.0001090155,0.0004909046,0.007197594,0.0001210203,0.1950094],"genre_scores_gemma":[0.07721147,0.00009317818,0.8649902,0.002612581,0.0004014116,0.0003915546,0.01032867,0.0003292239,0.04364168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1513677,"threshold_uncertainty_score":0.9998034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W25970070","doi":"10.1007/978-1-4613-0175-2_6","title":"Modelling of the Easter Effect","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Solar and Space Plasma Dynamics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Autoregressive integrated moving average; Component (thermodynamics); Econometrics; Computer science; Statistics; Mathematics; Time series; Physics; Thermodynamics","retraction":null,"screen_n_in":null,"score":{"opus":0.01312291166103557,"gpt":0.230072225994421,"spread":0.2169493143333854,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006888225,0.0002855559,0.0003816095,0.00005181182,0.00004111623,0.00001925568,0.0001890674,0.0001653939,0.0002800513],"category_scores_gemma":[0.00001628803,0.0001976175,0.0001271431,0.00003647633,0.0000771569,0.00001383874,0.00006017027,0.0006139534,0.0000146216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000281395,"about_ca_system_score_gemma":0.00004675622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001118586,"about_ca_topic_score_gemma":0.00006595853,"domain_scores_codex":[0.9991455,0.0000251334,0.0002509133,0.0002000343,0.0001962671,0.000182153],"domain_scores_gemma":[0.9988157,0.0005914884,0.0001858912,0.0003288844,0.00004958185,0.00002851665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004682119,0.00002969806,0.01241213,0.000139564,0.0001569779,0.00001116232,0.0003924944,0.6482483,0.00001119811,0.2884659,0.0001981211,0.0498876],"study_design_scores_gemma":[0.000492278,0.00007196653,0.00003239718,0.0003667623,0.0002190231,0.000002147564,0.000002672061,0.4143064,0.0001536617,0.576041,0.007838606,0.0004730403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004080328,0.00007815695,0.9117557,0.00002067632,0.0002728874,0.0002150346,0.0007913375,0.000006413512,0.08645175],"genre_scores_gemma":[0.9416581,0.0000315613,0.02776691,0.00008134096,0.0005518777,0.00001196357,0.0003968643,0.0001747242,0.02932669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.94125,"threshold_uncertainty_score":0.8058609,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W135223351","doi":"10.1007/978-1-4613-0049-6_7","title":"Additional Selected Topics","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Maximization; Computer science; TRACE (psycholinguistics); Variety (cybernetics); Block (permutation group theory); Mathematical optimization; Optimal design; Mathematics; Artificial intelligence; Combinatorics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.1127083287025158,"gpt":0.3942728854141296,"spread":0.2815645567116138,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004645802,0.0004693096,0.0006935297,0.000446149,0.00009288735,0.0001900422,0.0006934394,0.0006199037,0.3538904],"category_scores_gemma":[0.01428279,0.0004095106,0.0001079894,0.0003029014,0.0002419883,0.00007377109,0.0001464536,0.0009385272,0.001820049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002310931,"about_ca_system_score_gemma":0.0001587168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005517089,"about_ca_topic_score_gemma":0.00004939596,"domain_scores_codex":[0.9959198,0.000172601,0.0009634061,0.000810599,0.001769967,0.0003636711],"domain_scores_gemma":[0.9859672,0.01226474,0.0004264504,0.0006517057,0.0005574569,0.000132447],"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":[0.00003019847,0.00003818885,0.00001453609,0.00001117474,0.00003543269,0.0002398043,0.000169755,0.0003800362,0.00003068165,0.08154687,0.5835232,0.3339802],"study_design_scores_gemma":[0.0001244654,0.00008510942,0.00006683158,0.00005031097,0.00001210094,0.00002117808,9.690871e-7,0.002168739,0.00006170221,0.4516145,0.545468,0.0003260941],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[4.013908e-7,0.0003819849,0.6304445,0.0001454883,0.0005612327,0.0002862919,0.04578501,0.00005319437,0.3223419],"genre_scores_gemma":[0.0000609307,0.00004348313,0.7387856,0.000567626,0.0005047083,0.00002164357,0.002704267,0.00007434717,0.2572375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3700677,"threshold_uncertainty_score":0.9998357,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4236908534","doi":"10.1007/978-1-4613-0049-6_3","title":"Optimal Regression Designs in Asymmetric Domains","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Quadratic equation; Inverse; Regression; Linear regression; Polynomial regression; Optimality criterion; Minification; Reduction (mathematics); Regression analysis; Applied mathematics; Mathematical optimization; Statistics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.1633975620915099,"gpt":0.4262043656721061,"spread":0.2628068035805962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002583384,0.0007928645,0.001316606,0.002386573,0.000104924,0.0002676187,0.001214209,0.001023986,0.003214475],"category_scores_gemma":[0.01163412,0.0006276058,0.0001781557,0.000998304,0.0003395558,0.0001495154,0.0003330065,0.001571897,0.0006524986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005756356,"about_ca_system_score_gemma":0.0001410537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004564078,"about_ca_topic_score_gemma":0.00009918849,"domain_scores_codex":[0.993224,0.0005462985,0.001741468,0.001362671,0.002446107,0.0006794666],"domain_scores_gemma":[0.9838301,0.01385302,0.0007356996,0.00112588,0.0002433022,0.0002120246],"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":[0.0003419064,0.0002351672,0.0009540931,0.00005430302,0.00005183431,0.002804131,0.001487444,0.01781128,0.0005191442,0.09557174,0.009554106,0.8706148],"study_design_scores_gemma":[0.001813872,0.0008236857,0.0008378425,0.0006620535,0.00006619123,0.00009879805,0.00002818082,0.03345028,0.001894278,0.907635,0.0506987,0.001991167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003986319,0.002348522,0.8645375,0.00007560802,0.0006988363,0.0005786853,0.0004223229,0.00004127267,0.1312574],"genre_scores_gemma":[0.01030197,0.0003716161,0.9541565,0.0003194327,0.0001710291,0.00002127306,0.00006102858,0.0001456059,0.03445151],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8686237,"threshold_uncertainty_score":0.9996175,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W131830864","doi":"10.1007/978-1-4613-0175-2_5","title":"The Various Tables","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.08763052945530098,"gpt":0.3706667108680038,"spread":0.2830361814127029,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000900924,0.0002708717,0.0003375129,0.000146106,0.0003086191,0.0003226121,0.001003998,0.0003152497,0.0006269382],"category_scores_gemma":[0.003827869,0.0001656033,0.00007210011,0.0001774896,0.0003012127,0.00002325143,0.0001782399,0.0006442881,0.0002362225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007779716,"about_ca_system_score_gemma":0.000108991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008431637,"about_ca_topic_score_gemma":0.001626017,"domain_scores_codex":[0.9976518,0.00003341984,0.0007042945,0.0004688997,0.0008534957,0.0002880828],"domain_scores_gemma":[0.9900531,0.008222383,0.0003950349,0.0009806238,0.0002896325,0.00005924901],"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":[0.000005100747,0.000005348051,0.00003166107,0.000002175351,0.000006213518,0.00002194015,0.00004230228,0.0002837353,9.732457e-7,0.5577362,0.03356276,0.4083017],"study_design_scores_gemma":[0.000025253,0.0000188458,0.00001333457,0.00001828057,0.000007536575,0.00001128293,7.483383e-7,0.001185686,0.000003782216,0.5218421,0.4767676,0.0001055133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001304871,0.0005643722,0.7827913,0.0006825795,0.0001903054,0.0002508563,0.0006477947,0.0000536251,0.2148178],"genre_scores_gemma":[0.01303579,0.002546079,0.5086957,0.001491273,0.0009127853,0.000111261,0.0003429816,0.0002230127,0.4726411],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4432048,"threshold_uncertainty_score":0.6864534,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4237324981","doi":"10.1007/978-1-4613-0175-2_1","title":"Introduction","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.04269442971067006,"gpt":0.3199000194421774,"spread":0.2772055897315074,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000135435,0.0002693092,0.0003400579,0.0001667406,0.00004835789,0.00002398101,0.00007868132,0.0003617864,0.002740402],"category_scores_gemma":[0.0009038082,0.0002686639,0.00004440042,0.00004678173,0.00004505784,0.00003039082,0.00002247466,0.0004605752,0.0001029101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001631749,"about_ca_system_score_gemma":0.00003601746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001501649,"about_ca_topic_score_gemma":0.0003979236,"domain_scores_codex":[0.998811,0.00001807721,0.0004563773,0.000280956,0.000263765,0.0001698281],"domain_scores_gemma":[0.9986324,0.0006015965,0.0002772061,0.0003264936,0.000123954,0.00003835739],"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":[0.0000145089,0.00001588773,0.00006809471,0.0001247525,0.00001875752,0.00002452497,0.0001267944,0.0007424554,0.000002717697,0.888594,0.01952948,0.09073798],"study_design_scores_gemma":[0.0001212233,0.00002322323,0.00006142461,0.00005739845,0.00005031872,0.00002038737,2.673702e-7,0.001527458,0.00000712356,0.7756047,0.2223111,0.0002154103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001377747,0.0001248098,0.9220718,0.0006211693,0.0006849606,0.0002764538,0.0002820001,0.00007356401,0.07585147],"genre_scores_gemma":[0.009044648,0.0004994511,0.808046,0.000442768,0.007146678,0.00002126582,0.00348887,0.0003599542,0.1709503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2027816,"threshold_uncertainty_score":0.9999766,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4239009738","doi":"10.1007/978-1-4613-0111-0_2","title":"Reliability background","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Class (philosophy); Failure rate; Residual; Reliability (semiconductor); Distribution (mathematics); Portfolio; Stochastic ordering; Statistics; Reliability theory; Regular polygon; Applied mathematics; Computer science; Mathematical analysis; Economics; Physics; Algorithm; Thermodynamics; Artificial intelligence; Financial economics","retraction":null,"screen_n_in":null,"score":{"opus":0.08216684177435717,"gpt":0.3677752897054315,"spread":0.2856084479310743,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002096876,0.0004137015,0.0005420421,0.0001058506,0.00009914093,0.00005292874,0.0002262714,0.0004854599,0.006163371],"category_scores_gemma":[0.002758115,0.0004072292,0.00008198505,0.00009713662,0.0002541328,0.00003211059,0.00006058071,0.0007479849,0.0004367073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003206504,"about_ca_system_score_gemma":0.0001153677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001663712,"about_ca_topic_score_gemma":0.0001535331,"domain_scores_codex":[0.9979789,0.00003562527,0.0007657576,0.0004992757,0.0004118159,0.0003085738],"domain_scores_gemma":[0.9945542,0.00409623,0.0002899444,0.0006552644,0.0002736496,0.0001307418],"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":[0.00001052841,0.00005616905,0.000007584545,0.0001318044,0.00001605916,0.00002695815,0.00002924922,0.00006598282,0.000002084636,0.972795,0.009434547,0.01742409],"study_design_scores_gemma":[0.0002238624,0.00002680602,0.000157873,0.00009460001,0.00007623366,0.00001229084,0.000001381027,0.001889436,0.000005851594,0.9009452,0.09619955,0.0003668926],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000005538009,0.00005285389,0.822046,0.0002082768,0.0001215384,0.0004049877,0.004558504,0.00009882025,0.1725035],"genre_scores_gemma":[0.02298317,0.0002738509,0.8982013,0.0008984892,0.000455612,0.0001264938,0.004484916,0.0002644387,0.07231177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1001917,"threshold_uncertainty_score":0.9998379,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W118748914","doi":"10.1007/978-1-4613-0111-0_9","title":"Defective renewal equations","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Geometric distribution; Connection (principal bundle); Mathematics; Queueing theory; Geometric probability; Distribution (mathematics); Renewal theory; Expression (computer science); Geometric progression; Master equation; Applied mathematics; Geometric series; Probability distribution; Mathematical analysis; Computer science; Combinatorics; Geometry; Statistics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.1317787470076032,"gpt":0.3817506749713828,"spread":0.2499719279637796,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001127818,0.0003824955,0.0006400243,0.000435149,0.0001580897,0.000157399,0.0006537009,0.0006338057,0.001916397],"category_scores_gemma":[0.0128149,0.000299064,0.0001471319,0.0002197157,0.0003213978,0.00008035295,0.0001648481,0.0008607364,0.0005882325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002321768,"about_ca_system_score_gemma":0.0003080211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001097507,"about_ca_topic_score_gemma":0.006369389,"domain_scores_codex":[0.9964435,0.0001295856,0.0009331537,0.0008287971,0.00132628,0.000338725],"domain_scores_gemma":[0.9857945,0.01227091,0.0004023898,0.0009014992,0.0005170386,0.0001135983],"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":[0.00003682647,0.00003433828,0.0001498613,0.00001052994,0.00003035725,0.00008872252,0.0006920568,0.02359572,0.000002693976,0.750702,0.001510278,0.2231466],"study_design_scores_gemma":[0.0001698717,0.00007055508,0.00003653584,0.00005109472,0.00003644095,0.000008821921,0.000002979294,0.006296066,0.000008858779,0.9490888,0.0439042,0.0003257696],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000006833424,0.0004720836,0.791861,0.0002299456,0.0004274551,0.0002878774,0.001185306,0.00003152507,0.205498],"genre_scores_gemma":[0.3227313,0.001456602,0.3691998,0.002789107,0.001960024,0.00009054322,0.001070756,0.0003599547,0.300342],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4226612,"threshold_uncertainty_score":0.9999462,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4241079235","doi":"10.1007/978-1-4613-0175-2_4","title":"Moving Averages","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Seasonality; Series (stratigraphy); Moving average; A priori and a posteriori; Seasonal adjustment; Econometrics; Mathematics; Construct (python library); Simple (philosophy); Statistics; Computer science; Philosophy; Epistemology; Mathematical analysis; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01030989042104478,"gpt":0.2380761702126204,"spread":0.2277662797915757,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001093031,0.0002728441,0.000331448,0.00007330514,0.00007830818,0.00001707765,0.0002021225,0.0004314314,0.02535368],"category_scores_gemma":[0.0001505516,0.0002593227,0.00006123638,0.00006111076,0.0002234061,0.00003339167,0.0001260708,0.0005819589,0.001252835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001583678,"about_ca_system_score_gemma":0.00001176872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001804696,"about_ca_topic_score_gemma":0.003247542,"domain_scores_codex":[0.9988149,0.00002300439,0.0002638957,0.0003859006,0.0002468382,0.0002655052],"domain_scores_gemma":[0.9991279,0.0003909729,0.000120945,0.000294776,0.000006160128,0.00005924768],"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":[0.0000948522,0.0001109801,0.04193715,0.00009384287,0.0003394698,0.004130988,0.001594562,0.2200754,0.0001346231,0.05742437,0.02109231,0.6529714],"study_design_scores_gemma":[0.0002080032,0.00005876182,0.0009898717,0.00004298484,0.000158228,0.0000301349,7.09052e-7,0.007101676,0.00003532014,0.7073393,0.2834112,0.0006237974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00009911112,0.0002287234,0.4502388,0.0001313033,0.0001290094,0.00009688485,0.0001223784,0.00002964312,0.5489241],"genre_scores_gemma":[0.3429872,0.002283828,0.1427545,0.005691351,0.0008177458,0.00002264333,0.0009656447,0.0002952944,0.5041817],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6523477,"threshold_uncertainty_score":0.9999859,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}