{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":30,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":30,"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":"dd9011484c61","filters":{"venue":"Wiley Interdisciplinary Reviews Computational Statistics"}},"results":[{"id":"W2128728535","doi":"10.1002/wics.101","title":"Principal component analysis","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":10417,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Principal component analysis; Singular value decomposition; Correspondence analysis; Dimensionality reduction; Jackknife resampling; Multiple correspondence analysis; Mathematics; Multivariate statistics; Dimension (graph theory); Sparse PCA; Table (database); Similarity (geometry); Data set; Computer science; Statistics; Pattern recognition (psychology); Data mining; Artificial intelligence; Algorithm; Combinatorics","retraction":null,"screen_n_in":null,"score":{"opus":0.1009026244551046,"gpt":0.3998046590931104,"spread":0.2989020346380057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001091012,0.0008206284,0.004108726,0.0001475783,0.0005259628,0.000203129,0.0008185185,0.0004025316,0.002958422],"category_scores_gemma":[0.0002925668,0.0003219022,0.001894602,0.00183398,0.0002166094,0.00007457885,0.000684182,0.001007614,0.0006587856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089453,"about_ca_system_score_gemma":0.00004523717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001581255,"about_ca_topic_score_gemma":0.0002118543,"domain_scores_codex":[0.994281,0.0013383,0.002267619,0.001019332,0.0005906898,0.0005030545],"domain_scores_gemma":[0.9939778,0.003941542,0.001239672,0.0002526199,0.0002177485,0.0003706051],"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":[0.000005614696,0.000157959,0.000009440982,0.00109829,0.0006625013,0.0000361876,0.0000200325,0.00007909469,0.000001148308,0.003154491,0.002557304,0.992218],"study_design_scores_gemma":[0.00004250124,0.0001276546,0.0003079266,0.001599668,0.006353246,0.00003830338,0.00001119154,0.004279416,2.790844e-8,0.004764932,0.9817922,0.0006829859],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00000985597,0.9356087,0.05692543,0.0000506076,0.0003749421,0.0009156456,0.005710189,0.00006466393,0.0003399523],"genre_scores_gemma":[0.000007405019,0.9018785,0.08305265,0.00007126302,0.0004955966,0.0001525105,0.01405662,0.000007234697,0.0002782424],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9915349,"threshold_uncertainty_score":0.9999233,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2094013107","doi":"10.1002/wics.1246","title":"Multiple factor analysis: principal component analysis for multitable and multiblock data sets","year":2013,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":552,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Principal component analysis; Categorical variable; Table (database); Exploratory data analysis; Data set; Data mining; Contingency table; Statistics; Computer science; Factor analysis; Mathematics; Exploratory factor analysis; Set (abstract data type)","retraction":null,"screen_n_in":null,"score":{"opus":0.2573493746915503,"gpt":0.4304309503203014,"spread":0.1730815756287511,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001014762,0.0008867799,0.0048018,0.0002643854,0.0006314483,0.0003384687,0.001096474,0.0002934152,0.0009085739],"category_scores_gemma":[0.0007679247,0.0003864848,0.001289169,0.002132464,0.0001884134,0.0001973147,0.001517578,0.0003974442,0.0001046049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000103462,"about_ca_system_score_gemma":0.00003641087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001037931,"about_ca_topic_score_gemma":0.0007636761,"domain_scores_codex":[0.9937765,0.001144535,0.002344026,0.001661851,0.0004938784,0.0005791976],"domain_scores_gemma":[0.9882935,0.009095998,0.001363272,0.0004976293,0.0003381066,0.0004115543],"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":[0.00001464914,0.0002192166,0.0003738659,0.002355592,0.006526448,0.000007670124,0.00004118851,0.0006328248,0.000001702169,0.0001946121,0.002664516,0.9869677],"study_design_scores_gemma":[0.000144892,0.0001478031,0.002703573,0.000879872,0.02301002,0.00000784531,0.00003924406,0.4006194,3.96183e-8,0.0006557615,0.5710278,0.0007637313],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000559861,0.6910613,0.2585014,0.00004028522,0.0001214209,0.002015268,0.04815264,0.00004141328,0.00001033243],"genre_scores_gemma":[0.0001083581,0.6912009,0.2460439,0.00004535956,0.0001732111,0.0003288104,0.06193515,0.000008936056,0.0001553381],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.986204,"threshold_uncertainty_score":0.9998587,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963253923","doi":"10.1002/wics.1443","title":"Spatial modeling with R‐INLA: A review","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":410,"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":"Computer science; Inference; Code (set theory); Gaussian; Bayesian inference; Bayesian probability; Random field; Algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.05299260366295971,"gpt":0.346729488693779,"spread":0.2937368850308192,"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":[0.0007940757,0.0009537903,0.002755923,0.0001048119,0.000431412,0.00009792398,0.0007577608,0.0001714603,0.002327765],"category_scores_gemma":[0.000164483,0.0006933707,0.000407364,0.0005850505,0.0003591781,0.0001456344,0.001521373,0.0005581361,0.00490017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004475441,"about_ca_system_score_gemma":0.0001734907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007130361,"about_ca_topic_score_gemma":0.0001362531,"domain_scores_codex":[0.9950935,0.0004220287,0.001995032,0.001114837,0.0007988308,0.0005757512],"domain_scores_gemma":[0.9973828,0.000419565,0.00116576,0.0006399528,0.0001051892,0.0002867506],"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":[0.000006340798,0.0000676593,0.00000381429,0.02784004,0.00009228869,0.0000606932,0.00004989972,0.0009176409,3.00182e-9,0.0002681725,0.1447557,0.8259377],"study_design_scores_gemma":[0.0001189568,0.0002143853,0.000002078337,0.1166148,0.001066506,0.0002406054,0.000004254219,0.04189641,1.583973e-9,0.002979752,0.8360888,0.000773426],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[5.643641e-8,0.570216,0.4263841,0.00003067179,0.0002142926,0.001363058,0.0006046395,0.00003627618,0.001150924],"genre_scores_gemma":[7.767147e-7,0.888755,0.1071039,0.0002935374,0.0002887909,0.0004076039,0.002759358,0.0001117685,0.0002792377],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8251643,"threshold_uncertainty_score":0.9995518,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2112662632","doi":"10.1002/wics.198","title":"STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling","year":2012,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":137,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Principal component analysis; Multidimensional scaling; Linear discriminant analysis; Metric (unit); Similarity (geometry); Computer science; Mathematics; Set (abstract data type); Contingency table; Algorithm; Data mining; Statistics; Artificial intelligence; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.05564053156836534,"gpt":0.3654869761204985,"spread":0.3098464445521332,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006869481,0.001168528,0.004283763,0.00115094,0.0006213439,0.0001964354,0.0004051974,0.0003478951,0.001910887],"category_scores_gemma":[0.0001875196,0.0009659724,0.0006545908,0.001816373,0.0003987187,0.0002405048,0.001402917,0.0008360066,0.0001507464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004109033,"about_ca_system_score_gemma":0.0001233843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007053007,"about_ca_topic_score_gemma":0.00006205119,"domain_scores_codex":[0.9946965,0.000225958,0.002346543,0.001226168,0.0007514266,0.0007534018],"domain_scores_gemma":[0.994479,0.00264353,0.001560709,0.0005278441,0.0002323728,0.0005565382],"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":[0.0000280778,0.0004481373,0.0009705091,0.02710559,0.00538931,0.00005735791,0.0002491647,0.0007498507,0.00000178709,0.001493246,0.001323459,0.9621835],"study_design_scores_gemma":[0.001695022,0.0002375522,0.002068657,0.01309266,0.1016304,0.0005579855,0.0003020626,0.1210843,0.00000467009,0.003767773,0.7507758,0.004783091],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004884416,0.7358589,0.259073,0.00001110205,0.0001000327,0.0004540683,0.00427055,0.00005324515,0.0001303118],"genre_scores_gemma":[0.0002172717,0.7899705,0.202081,0.00001886213,0.0001659167,0.0001783694,0.007021882,0.00009071232,0.0002555803],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9574004,"threshold_uncertainty_score":0.9992791,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2936732373","doi":"10.1002/wics.1462","title":"Insurance risk assessment in the face of climate change: Integrating data science and statistics","year":2019,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Actua; Agriculture and Agri-Food Canada","funders":"National Science Foundation","keywords":"Damages; Climate change; Multidisciplinary approach; Extreme weather; Actuarial science; Risk assessment; Risk analysis (engineering); Environmental resource management; Environmental science; Computer science; Business; Political science; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.1902798057844051,"gpt":0.4264089183153925,"spread":0.2361291125309874,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003414549,0.0005578657,0.001714791,0.00007684231,0.0004080403,0.0002634356,0.001929641,0.0001447048,0.00005094811],"category_scores_gemma":[0.0007609624,0.0001887237,0.0001033697,0.001326118,0.0004301743,0.0004851812,0.002351285,0.0008497962,0.00003240664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001863248,"about_ca_system_score_gemma":0.000123862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005942314,"about_ca_topic_score_gemma":0.000477471,"domain_scores_codex":[0.9954111,0.0008075103,0.001522162,0.0008724164,0.0009027736,0.000484045],"domain_scores_gemma":[0.994234,0.003056193,0.001853131,0.0003711343,0.0003737866,0.0001118122],"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":[0.000003769065,0.0001032773,0.0002967493,0.004844496,0.00001610591,0.000008546412,0.000446643,0.000006000642,0.000001013369,0.0009609124,0.002236186,0.9910763],"study_design_scores_gemma":[0.0004313132,0.001216271,0.04759699,0.06703153,0.001109074,0.0003294091,0.003714022,0.02064298,6.580909e-8,0.00357572,0.8524156,0.001937085],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001125742,0.9243754,0.0009896534,0.0001915656,0.0002689285,0.002881929,0.07102537,0.00001679326,0.0001377897],"genre_scores_gemma":[0.0002395149,0.9747295,0.01586451,0.00009136228,0.0001769151,0.0001364768,0.008751902,0.00000571939,0.00000414734],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9891392,"threshold_uncertainty_score":0.7695931,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803390963","doi":"10.1002/wics.1434","title":"A review of quadratic discriminant analysis for high‐dimensional data","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadratic classifier; Exploratory data analysis; Linear discriminant analysis; Curse of dimensionality; Clustering high-dimensional data; Cluster analysis; Mathematics; Covariance; Artificial intelligence; Bayesian probability; Graphical model; Quadratic equation; Machine learning; Computer science; Pattern recognition (psychology); Data mining; Statistics; Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.1476410628016731,"gpt":0.4255622137364176,"spread":0.2779211509347445,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00172307,0.0007152704,0.004113819,0.0005072938,0.0002755628,0.0001017221,0.002969475,0.0001787896,0.000153984],"category_scores_gemma":[0.0005107548,0.0005205825,0.0009423168,0.001500855,0.0001862057,0.0004442999,0.003481009,0.0002757071,0.0002460802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059924,"about_ca_system_score_gemma":0.0005200643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006612264,"about_ca_topic_score_gemma":0.00001100686,"domain_scores_codex":[0.9937223,0.0006887182,0.003178885,0.001380707,0.0006381334,0.0003912174],"domain_scores_gemma":[0.9926779,0.001744423,0.002627498,0.002055329,0.0007105125,0.0001843323],"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":[0.000004211868,0.0001408705,2.704726e-7,0.1383613,0.0006229581,0.000005420913,0.00003208232,0.00003387885,2.84886e-8,0.002592497,0.2758474,0.5823591],"study_design_scores_gemma":[0.0001476943,0.0002388158,0.000002871867,0.2605545,0.006075969,0.00003984299,0.000003000579,0.0483759,8.090747e-8,0.01178368,0.6721605,0.0006171412],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.125222e-8,0.5102967,0.4826746,0.00007464377,0.0003361417,0.0012776,0.005305186,0.00002246912,0.00001260621],"genre_scores_gemma":[2.724526e-7,0.6464141,0.3270895,0.000210983,0.0001240325,0.0002890353,0.02579351,0.00002796743,0.00005064151],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5817419,"threshold_uncertainty_score":0.9997246,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124988298","doi":"10.1002/wics.165","title":"Computations using analysis of covariance","year":2011,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Prince Edward Island; University of New Brunswick","funders":"","keywords":"Analysis of covariance; Covariate; Statistics; Nonparametric statistics; Regression analysis; Mathematics; Analysis of variance; Linear regression; Econometrics; Covariance; Statistical hypothesis testing","retraction":null,"screen_n_in":null,"score":{"opus":0.3841182895742637,"gpt":0.5345027831583571,"spread":0.1503844935840934,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009870772,0.0008143503,0.005896902,0.0008249441,0.0002501233,0.00003657658,0.0006144593,0.000261222,0.0004095736],"category_scores_gemma":[0.0008325352,0.0006835426,0.001282623,0.001916581,0.0003359491,0.000123063,0.0006420694,0.0005195458,0.00003825901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000229239,"about_ca_system_score_gemma":0.0003065137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008190867,"about_ca_topic_score_gemma":0.00001127859,"domain_scores_codex":[0.9937243,0.001062295,0.003554794,0.0008146463,0.0004230473,0.0004209013],"domain_scores_gemma":[0.9897634,0.005859761,0.003005176,0.0006625921,0.000494444,0.0002145821],"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":[0.000008100547,0.000214059,6.560261e-7,0.01475274,0.002006126,0.0000158058,0.0002226351,0.004375688,2.950664e-8,0.3390554,0.001403989,0.6379448],"study_design_scores_gemma":[0.0001419537,0.0001115269,0.000002538927,0.01549547,0.02419038,0.0000417378,0.00001896799,0.1210917,2.195018e-8,0.6523072,0.1858377,0.0007608281],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[1.206712e-7,0.4763029,0.5181958,0.000001362818,0.000163239,0.0007089218,0.00448327,0.0000250805,0.0001192904],"genre_scores_gemma":[5.846256e-7,0.4874575,0.5112702,0.000009038024,0.00004486429,0.00006427617,0.001050885,0.0000571686,0.00004545957],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.637184,"threshold_uncertainty_score":0.9995615,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2781990454","doi":"10.1002/wics.1423","title":"Computational methods for birth‐death processes","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Human Genome Research Institute; Dalhousie University; National Institutes of Health; National Science Foundation","keywords":"Inference; Birth–death process; Computer science; Statistical inference; Simple (philosophy); Hidden Markov model; Poisson distribution; Artificial intelligence; Algorithm; Machine learning; Statistical physics; Mathematics; Statistics; Population; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.08468246692179184,"gpt":0.4729340910250185,"spread":0.3882516241032267,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001020296,0.0007304958,0.001668253,0.000199017,0.0003963977,0.0001390224,0.0007157903,0.0003375901,0.000101556],"category_scores_gemma":[0.0008660763,0.0006072213,0.0006160726,0.0003183155,0.0002498054,0.00001085022,0.0009416194,0.0002667356,0.000152922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009757283,"about_ca_system_score_gemma":0.001027609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.719701e-7,"about_ca_topic_score_gemma":0.000006331988,"domain_scores_codex":[0.9961218,0.0006028924,0.001513039,0.001004659,0.000299316,0.0004582823],"domain_scores_gemma":[0.9967776,0.000839947,0.0008534886,0.0004297122,0.0008634096,0.0002358257],"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":[0.0000403505,0.000186323,0.000001476852,0.02442086,0.0002576128,0.000003545071,0.00005225613,0.0005815538,4.262646e-7,0.002070989,0.07613282,0.8962518],"study_design_scores_gemma":[0.0002736069,0.0005566166,0.00000153359,0.005442367,0.0003055779,0.00009699618,0.00001022038,0.01290363,2.26646e-7,0.0160613,0.963712,0.000635908],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[3.085769e-7,0.5034556,0.492453,0.00001644467,0.0002435754,0.001205448,0.002478519,0.00001656065,0.0001305219],"genre_scores_gemma":[2.735444e-7,0.5725594,0.4069987,0.0001054332,0.0004201458,0.0004334666,0.01841305,0.0000799341,0.0009895401],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8956159,"threshold_uncertainty_score":0.9996379,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410790146","doi":"10.1002/wics.70028","title":"A Review of Benchmark and Test Functions for Global Optimization Algorithms and Metaheuristics","year":2025,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Metaheuristic; Benchmark (surveying); Computer science; Algorithm; Global optimization; Test functions for optimization; Mathematical optimization; Mathematics; Optimization problem; Multi-swarm optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.04656249920066885,"gpt":0.3987294413001376,"spread":0.3521669420994687,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001445914,0.0006946964,0.002972453,0.000343874,0.0003507148,0.0002020601,0.000871332,0.0002002737,0.00004638796],"category_scores_gemma":[0.004496236,0.0006018364,0.0003611553,0.001414155,0.0002787847,0.0002414053,0.001819649,0.0003195767,0.000008900359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001755216,"about_ca_system_score_gemma":0.0008109689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001942355,"about_ca_topic_score_gemma":0.000001483918,"domain_scores_codex":[0.9949201,0.0005885509,0.0025235,0.001063498,0.000524405,0.0003799566],"domain_scores_gemma":[0.9901457,0.006340643,0.001437151,0.0006716297,0.001148168,0.0002566595],"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":[0.000001905364,0.00009993841,0.000001672459,0.188936,0.0001239177,0.000003918515,0.00001320885,0.0007409468,1.329489e-9,0.00932475,0.03567919,0.7650746],"study_design_scores_gemma":[0.0002494158,0.0002508623,0.000002694427,0.1254591,0.001189676,0.0001300604,0.000003966773,0.3881945,5.465265e-9,0.004362205,0.4796653,0.0004922616],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[4.442748e-10,0.4972821,0.4972131,0.00008587755,0.0002331853,0.001908682,0.003157472,0.00002524181,0.00009436464],"genre_scores_gemma":[8.268027e-9,0.5269803,0.4705775,0.00008793968,0.0000456339,0.0003583495,0.001769947,0.00001694887,0.0001633849],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7645823,"threshold_uncertainty_score":0.9996433,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2151514717","doi":"10.1002/wics.1232","title":"Shrinkage and absolute penalty estimation in linear regression models","year":2012,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Estimator; Lasso (programming language); Least absolute deviations; Linear regression; Shrinkage; Linear model; Regression; Regression analysis; Statistics; Mathematics; Proper linear model; Shrinkage estimator; Computer science; Bias of an estimator; Polynomial regression","retraction":null,"screen_n_in":null,"score":{"opus":0.2291040509500408,"gpt":0.4683546641537957,"spread":0.2392506132037548,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001705883,0.0008174736,0.002943611,0.0003182593,0.0002078058,0.0000799606,0.0003759108,0.000327633,0.0002114091],"category_scores_gemma":[0.0008864251,0.0006060392,0.0002583748,0.0003861924,0.0001951861,0.000241246,0.0008701126,0.0008566885,0.0001403215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000239875,"about_ca_system_score_gemma":0.0001558112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000591349,"about_ca_topic_score_gemma":0.0000109997,"domain_scores_codex":[0.9947272,0.001058283,0.0025069,0.000710343,0.0004830771,0.0005141767],"domain_scores_gemma":[0.9931381,0.00472952,0.001263989,0.0004438393,0.0001487034,0.0002758686],"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":[0.000008120136,0.0001347837,0.000001357372,0.02093038,0.00003951158,0.00002426996,0.0002366528,0.000150431,1.096303e-8,0.1001136,0.003223689,0.8751372],"study_design_scores_gemma":[0.0002084009,0.0001015723,0.00001850567,0.04100814,0.0006331897,0.0001289246,0.00001368351,0.1834989,1.092041e-8,0.689723,0.08398083,0.0006848536],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[7.487673e-7,0.5167159,0.4812095,0.00001220619,0.0002329939,0.0008914025,0.0006483486,0.00002770806,0.000261153],"genre_scores_gemma":[0.000004591818,0.524698,0.4743733,0.00001531736,0.00008910911,0.0001589203,0.0005201155,0.00005504103,0.00008556605],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8744523,"threshold_uncertainty_score":0.9996391,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136524907","doi":"10.1002/wics.1288","title":"Least angle regression for model selection","year":2014,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Lasso (programming language); Model selection; Regression diagnostic; Regression analysis; Statistical model; Computer science; Selection (genetic algorithm); Proper linear model; Regression; Exploratory data analysis; Linear regression; Statistics; Graphical model; Artificial intelligence; Machine learning; Mathematics; Polynomial regression","retraction":null,"screen_n_in":null,"score":{"opus":0.2421869945452439,"gpt":0.4896456163323069,"spread":0.247458621787063,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001391291,0.0008965693,0.003602684,0.0002298598,0.0004582483,0.0001143324,0.0005266697,0.0003635332,0.0001741796],"category_scores_gemma":[0.002168533,0.0006574874,0.0007471116,0.0003363274,0.0001526248,0.00008208696,0.0004557907,0.0005849811,0.0001602455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002935572,"about_ca_system_score_gemma":0.0003097459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.452264e-7,"about_ca_topic_score_gemma":0.000004490101,"domain_scores_codex":[0.9949674,0.0007655284,0.002419839,0.0008848397,0.0004582204,0.0005041774],"domain_scores_gemma":[0.9905749,0.006610345,0.001690607,0.0004265306,0.0004634823,0.0002341356],"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":[0.00001280611,0.0001012091,1.937144e-7,0.0283118,0.00006615731,0.000001707144,0.0000517017,0.0001646237,4.184896e-8,0.1722108,0.09937199,0.699707],"study_design_scores_gemma":[0.0001267536,0.0001726025,1.85133e-7,0.01901433,0.0006119575,0.0000373468,0.000003503,0.1625978,2.845665e-8,0.4176337,0.3993751,0.0004267483],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[4.821494e-8,0.43943,0.556647,0.00001523692,0.0002641559,0.001505613,0.001824186,0.00005694481,0.0002568659],"genre_scores_gemma":[2.197222e-7,0.4756607,0.5217992,0.00003169813,0.0002726681,0.000579662,0.001033459,0.00009031408,0.0005320521],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6992802,"threshold_uncertainty_score":0.9995877,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1975463773","doi":"10.1002/wics.1331","title":"Robust dimension reduction","year":2014,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Principal component analysis; Singular value decomposition; Sparse PCA; Nonlinear dimensionality reduction; Kernel (algebra); Covariance matrix; Dimension (graph theory); Kernel principal component analysis; Mathematics; Projection (relational algebra); Computer science; Pattern recognition (psychology); Random projection; Artificial intelligence; Kernel method; Algorithm; Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.250175406344999,"gpt":0.4845106984488016,"spread":0.2343352921038027,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001417363,0.001024092,0.004268388,0.0002637844,0.0004276707,0.00008946924,0.0005042579,0.0003607171,0.000265917],"category_scores_gemma":[0.001224537,0.0007989547,0.0007209818,0.0003922168,0.0002353867,0.0001398706,0.0007595058,0.0008753381,0.0004919534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003547386,"about_ca_system_score_gemma":0.0001725699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001208312,"about_ca_topic_score_gemma":0.000001908579,"domain_scores_codex":[0.993463,0.001428096,0.002891715,0.001087016,0.0005891463,0.000540967],"domain_scores_gemma":[0.9927113,0.004129111,0.001853798,0.0007080199,0.0002984692,0.0002992782],"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":[0.000007559026,0.0001071905,2.37775e-8,0.02101561,0.00008607931,0.00001705263,0.00006045167,0.0003627246,3.241442e-8,0.1328493,0.05638463,0.7891093],"study_design_scores_gemma":[0.0001050103,0.000120699,8.488428e-8,0.01921451,0.0007855218,0.0001927722,0.000008139449,0.004882521,1.582624e-8,0.4390631,0.5350916,0.0005361008],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[2.436813e-8,0.4853547,0.5120327,0.00001456547,0.0005810535,0.001047533,0.000637071,0.00006996324,0.0002624427],"genre_scores_gemma":[9.929783e-8,0.5121552,0.4856879,0.00001673523,0.0003365499,0.0002021575,0.001103126,0.0001024371,0.0003958393],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7885732,"threshold_uncertainty_score":0.9994462,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1939513865","doi":"10.1002/wics.1362","title":"Use of majority votes in statistical learning","year":2015,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Popularity; Gradient boosting; Computer science; Cluster analysis; Random forest; Machine learning; Artificial intelligence; Aggregate (composite); Exploratory data analysis; Ensemble learning; Data science; Data mining; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.3781482536796996,"gpt":0.4937488496305504,"spread":0.1156005959508508,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002277675,0.0008287212,0.004860363,0.0003901396,0.0001132117,0.00009146384,0.0005057966,0.0003402471,0.0003777258],"category_scores_gemma":[0.009994639,0.0006731281,0.0003560022,0.0006550735,0.000388962,0.00013763,0.0008972684,0.001298726,0.0001404809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003539746,"about_ca_system_score_gemma":0.0005287018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002566418,"about_ca_topic_score_gemma":0.00002278628,"domain_scores_codex":[0.9916112,0.002629667,0.00370797,0.0007716768,0.0007627128,0.000516756],"domain_scores_gemma":[0.9791006,0.01792603,0.001739293,0.0004575449,0.0004899892,0.0002865297],"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":[0.00001671956,0.0002291516,0.0000228508,0.02138965,0.00007390567,0.00006477819,0.0001420054,0.00008465232,1.590029e-8,0.1681834,0.01644675,0.7933461],"study_design_scores_gemma":[0.0001738397,0.0003066568,0.00002417538,0.02416946,0.0005590664,0.00005938977,0.0000237789,0.007358074,1.78697e-8,0.4952524,0.4714682,0.0006049639],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.596128e-7,0.521097,0.4746526,0.000006574641,0.0002072063,0.0009434069,0.002912291,0.00002820899,0.0001518343],"genre_scores_gemma":[0.000001405297,0.515549,0.4832236,0.000007174987,0.00006708643,0.0001026033,0.0008920213,0.00005995853,0.0000971043],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7927411,"threshold_uncertainty_score":0.999572,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2791397484","doi":"10.1002/wics.1432","title":"Estimation and testing for separable variance–covariance structures","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Covariance; Mathematics; Kronecker product; Variance (accounting); Covariance matrix; Estimator; Statistics; Multivariate normal distribution; Separable space; Normality; Estimation of covariance matrices; Rational quadratic covariance function; Applied mathematics; Multivariate statistics; Covariance intersection; Kronecker delta","retraction":null,"screen_n_in":null,"score":{"opus":0.25947355111828,"gpt":0.5121512017751574,"spread":0.2526776506568774,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00125626,0.00080423,0.002756797,0.0001494436,0.0005668648,0.0001465857,0.0003587121,0.0002632875,0.00004904281],"category_scores_gemma":[0.004786106,0.0006518477,0.0002680178,0.0003245741,0.0002764361,0.0001771885,0.0004571292,0.000397274,0.00003159415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001748068,"about_ca_system_score_gemma":0.0002310981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001290618,"about_ca_topic_score_gemma":0.000002385852,"domain_scores_codex":[0.9956886,0.0005103445,0.002033437,0.0009654438,0.0003190792,0.0004831015],"domain_scores_gemma":[0.9857319,0.01173655,0.001511482,0.0004197568,0.0003987079,0.0002016069],"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":[0.00001173239,0.00003805733,1.018026e-7,0.02828971,0.00008872836,0.000006203452,0.00007752253,0.0002945158,3.335489e-8,0.1682204,0.01465037,0.7883226],"study_design_scores_gemma":[0.0001550753,0.0001989746,4.671161e-7,0.0132954,0.0005557567,0.00009525722,0.000004988805,0.09880331,3.060235e-8,0.6513313,0.235105,0.0004544425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[6.076383e-8,0.4651924,0.5306048,0.000009163979,0.0002594688,0.001600358,0.002216641,0.00004345525,0.00007363458],"genre_scores_gemma":[2.253711e-7,0.4167712,0.58161,0.00002513775,0.0002567337,0.0004471024,0.00066042,0.00008039612,0.0001487507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7878682,"threshold_uncertainty_score":0.9995933,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4220771592","doi":"10.1002/wics.1580","title":"Function minimization and nonlinear least squares in R","year":2022,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Minification; Non-linear least squares; Nonlinear programming; Mathematical optimization; Context (archaeology); Computer science; Nonlinear system; Perspective (graphical); Least-squares function approximation; Function (biology); Implementation; Algorithm; Mathematics; Estimation theory; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1448641518111891,"gpt":0.4485202495835514,"spread":0.3036560977723624,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008075165,0.000632713,0.002040896,0.0006712206,0.000358561,0.0001025845,0.0003720786,0.0001879026,0.001166903],"category_scores_gemma":[0.0008981244,0.0005876693,0.0002303564,0.0009684564,0.0001569861,0.0002178663,0.001108955,0.0008952989,0.00008400596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005141045,"about_ca_system_score_gemma":0.0002785016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001426163,"about_ca_topic_score_gemma":0.00002102422,"domain_scores_codex":[0.9953165,0.0009988963,0.001821747,0.0007988027,0.0006752583,0.0003888202],"domain_scores_gemma":[0.9955247,0.002779695,0.001013949,0.0003755732,0.0001524656,0.000153587],"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":[0.00002109543,0.000193597,0.0000026744,0.01409311,0.00006190009,0.0000371685,0.0001811164,0.0030901,3.26298e-9,0.006622327,0.01173647,0.9639604],"study_design_scores_gemma":[0.0003018583,0.0001549461,0.000002594091,0.006239644,0.0002516398,0.00009720548,0.00009157307,0.06310919,2.615055e-9,0.04535395,0.8838857,0.0005117281],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.669406e-7,0.6339368,0.363268,0.0000353839,0.000219234,0.001514984,0.0008470656,0.00004430816,0.0001340108],"genre_scores_gemma":[9.011475e-8,0.7188862,0.2760495,0.00002214716,0.0001198611,0.0005009029,0.003904589,0.0001009125,0.0004158512],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9634487,"threshold_uncertainty_score":0.9997461,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402383094","doi":"10.1002/wics.70002","title":"Convergence rates of Metropolis–Hastings algorithms","year":2024,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Metropolis–Hastings algorithm; Convergence (economics); Algorithm; Computer science; Mathematics; Markov chain Monte Carlo; Artificial intelligence; Bayesian probability; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.230977783665814,"gpt":0.5139425053265814,"spread":0.2829647216607674,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001819085,0.0009966507,0.004471656,0.0004477533,0.0001560651,0.00008592325,0.0007564087,0.0002943085,0.0002157379],"category_scores_gemma":[0.00106062,0.0007307596,0.001186545,0.0008136293,0.0003232443,0.00009397275,0.001260738,0.0007824677,0.00002694547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002658193,"about_ca_system_score_gemma":0.0003658591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007300749,"about_ca_topic_score_gemma":0.000008302958,"domain_scores_codex":[0.9938467,0.0008899839,0.003347425,0.0008535527,0.0006143281,0.0004480103],"domain_scores_gemma":[0.9932879,0.003542124,0.00193868,0.000599171,0.0004205448,0.0002116342],"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":[0.000005034955,0.0001071452,6.286256e-7,0.09799841,0.0003843146,0.00005597665,0.0002325566,0.000007600549,9.188539e-8,0.05090177,0.1375393,0.7127672],"study_design_scores_gemma":[0.0001149075,0.0001819591,1.533444e-7,0.0602179,0.003110443,0.0001351398,0.00007524601,0.005317253,3.044061e-7,0.08951295,0.8406078,0.0007259494],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.638393e-7,0.6631416,0.3316488,0.00001391249,0.001044616,0.001077115,0.002538186,0.0000535351,0.0004819806],"genre_scores_gemma":[4.539861e-7,0.6459813,0.3517897,0.00001464221,0.0002747405,0.0001746425,0.000746322,0.0001125799,0.0009056544],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7120413,"threshold_uncertainty_score":0.9995143,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410790021","doi":"10.1002/wics.70029","title":"Orthogonal Arrays: A Review","year":2025,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Orthogonal array; Mathematics; Statistics; Taguchi methods","retraction":null,"screen_n_in":null,"score":{"opus":0.2400532549990042,"gpt":0.5504337736566163,"spread":0.3103805186576121,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.008210615,0.001409199,0.007735809,0.0008909384,0.0004926102,0.0004314199,0.003201994,0.0003575122,0.002822968],"category_scores_gemma":[0.007419619,0.0009924758,0.002181906,0.003123459,0.0003864056,0.0003322473,0.0029936,0.00109029,0.005746278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004666136,"about_ca_system_score_gemma":0.001398807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001264008,"about_ca_topic_score_gemma":0.000002550552,"domain_scores_codex":[0.9823223,0.005331062,0.006987534,0.002064332,0.002621068,0.0006736486],"domain_scores_gemma":[0.9793465,0.01435831,0.003359497,0.001665694,0.000862843,0.0004071287],"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":[0.000005339739,0.00008856019,4.054522e-7,0.03270904,0.00008181383,0.00004327311,0.00002586388,0.00004043094,1.509684e-8,0.006527662,0.3113687,0.6491088],"study_design_scores_gemma":[0.00009463942,0.0001114014,8.451106e-7,0.1819704,0.0007492498,0.0001315801,0.00001322948,0.0004833086,1.337868e-8,0.0511394,0.7646208,0.0006850873],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.825202e-9,0.6195427,0.3703772,0.00009423895,0.001000512,0.002534584,0.002109352,0.00005229729,0.004289204],"genre_scores_gemma":[9.559096e-9,0.6613393,0.3324265,0.000621587,0.000195727,0.0006426314,0.001661117,0.00005980347,0.003053211],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.6484238,"threshold_uncertainty_score":0.9998658,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2790909449","doi":"10.1002/wics.110","title":"Likelihood inference","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Likelihood function; Likelihood principle; Inference; Empirical likelihood; Maximum likelihood; Restricted maximum likelihood; Marginal likelihood; Parametric statistics; Quasi-maximum likelihood; Computer science; Bayesian inference; Bayesian probability; Likelihood-ratio test; Econometrics; Mathematics; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1195520516965725,"gpt":0.4714508139783933,"spread":0.3518987622818208,"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":[0.001335473,0.001212363,0.004457922,0.0003160555,0.0003920186,0.0002296183,0.001126884,0.0006167261,0.001376206],"category_scores_gemma":[0.003690544,0.0009275607,0.0007706875,0.0005884494,0.0003758698,0.0001394243,0.001189539,0.002016577,0.001352201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001949778,"about_ca_system_score_gemma":0.0006233756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002907477,"about_ca_topic_score_gemma":0.0000179784,"domain_scores_codex":[0.9933428,0.001060614,0.003109286,0.001061327,0.0006808155,0.0007451588],"domain_scores_gemma":[0.9849746,0.01152077,0.001792177,0.0009024847,0.0003693473,0.0004405669],"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":[0.000003018764,0.0001283165,7.53511e-7,0.013742,0.00006470622,0.00004189574,0.00005682043,2.85247e-7,2.136598e-8,0.2681442,0.02091159,0.6969063],"study_design_scores_gemma":[0.00007382769,0.00008810631,0.000001084756,0.01515743,0.0005806178,0.00008891142,0.000004815966,0.0003572018,1.409333e-8,0.4983938,0.4847027,0.0005514792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[2.080204e-8,0.4919538,0.503674,0.00001587341,0.0005998474,0.0009899584,0.001935212,0.00007373348,0.00075763],"genre_scores_gemma":[7.44535e-8,0.5088492,0.4896859,0.00003178956,0.0002885593,0.0002696415,0.0006973747,0.00008552612,0.00009192135],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.6963549,"threshold_uncertainty_score":0.9995367,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362576321","doi":"10.1002/wics.1606","title":"Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging","year":2023,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of Neurological Disorders and Stroke; Northern California Institute for Research and Education; University of Southern California; Biogen; Emory University; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Neuroimaging; Positron emission tomography; Alzheimer's Disease Neuroimaging Initiative; Neuroscience; Dementia; Voxel; Alzheimer's disease; Psychology; Functional neuroimaging; Cognition; Disease; Medicine; Artificial intelligence; Computer science; Cognitive impairment; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.246345841401849,"gpt":0.4446603429112698,"spread":0.1983145015094208,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001283067,0.001036959,0.003539797,0.0004648346,0.0003878941,0.0001275008,0.0006356786,0.0001539821,0.00006085881],"category_scores_gemma":[0.002601202,0.0008550849,0.0008813511,0.0005596671,0.0004293668,0.0001335979,0.0008778907,0.0006641808,0.00002862281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009340476,"about_ca_system_score_gemma":0.0002399135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002339543,"about_ca_topic_score_gemma":7.465907e-7,"domain_scores_codex":[0.9931599,0.001034965,0.003404264,0.001098567,0.0006189608,0.0006832904],"domain_scores_gemma":[0.982244,0.0143742,0.002074145,0.0005602509,0.0002918524,0.0004555349],"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":[0.00003871573,0.0001638355,0.00005870999,0.03300642,0.0002300557,0.00006597655,0.0001122828,0.00010032,1.244443e-7,0.02627944,0.005641462,0.9343026],"study_design_scores_gemma":[0.000305127,0.0002347137,0.0001373383,0.03205629,0.004888115,0.00008605937,0.00003469528,0.4274221,1.214302e-7,0.5235323,0.01027391,0.001029156],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001252949,0.4518251,0.5412793,0.00001793816,0.0003843512,0.00168508,0.004709892,0.00007569031,0.00002147516],"genre_scores_gemma":[0.0000540464,0.3820312,0.6135597,0.00002381232,0.0002140585,0.0006468955,0.003203859,0.0002432991,0.00002310376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9332735,"threshold_uncertainty_score":0.99939,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2791727491","doi":"10.1002/wics.1433","title":"In defense of Pratt's variable importance axioms: A response to Gromping","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; Carleton University","funders":"","keywords":"Metric (unit); Computer science; Variable (mathematics); Exploratory data analysis; Heuristic; Inference; Statistical inference; GRASP; Data science; Machine learning; Simple (philosophy); Axiom; Variables; Data mining; Artificial intelligence; Statistics; Mathematics; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.1785006596525015,"gpt":0.499036579587775,"spread":0.3205359199352735,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003601206,0.0008223053,0.004103546,0.0004966435,0.0001455052,0.00004349522,0.0006489248,0.0002697781,0.0002685048],"category_scores_gemma":[0.005954433,0.0006994657,0.0003810444,0.001053773,0.0002344384,0.0001676724,0.0009262004,0.0005716422,0.0001438644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000437936,"about_ca_system_score_gemma":0.000512254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000401068,"about_ca_topic_score_gemma":0.00001565424,"domain_scores_codex":[0.9921334,0.001899706,0.003851016,0.0009939688,0.0005396978,0.000582211],"domain_scores_gemma":[0.9863591,0.0103833,0.001827477,0.0007401963,0.0004106329,0.0002793559],"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":[0.0005040102,0.0006212806,0.000001513057,0.06384662,0.0002445844,0.0002595254,0.001352351,0.0005268058,8.105796e-7,0.4194018,0.05621593,0.4570248],"study_design_scores_gemma":[0.0002024529,0.0003683273,0.000001147958,0.03867767,0.0003249132,0.00009066137,0.00002894766,0.001872824,5.729744e-8,0.5700921,0.3877768,0.0005640637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001044255,0.4348018,0.5612903,0.00002464199,0.0002298593,0.001555245,0.001895633,0.0000199536,0.0001815501],"genre_scores_gemma":[9.563412e-7,0.3961063,0.6029087,0.00006836706,0.0001165364,0.0003259478,0.0001986416,0.0000876772,0.0001869233],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4564607,"threshold_uncertainty_score":0.9995456,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406154750","doi":"10.1002/wics.70008","title":"Utilizing Machine Learning for Early Intervention and Risk Management in the Opioid Overdose Crisis","year":2025,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Opioid overdose; Opioid; Intervention (counseling); Crisis management; Crisis intervention; Computer science; Medicine; Artificial intelligence; Machine learning; Psychiatry; Economics; (+)-Naloxone; Internal medicine; Management","retraction":null,"screen_n_in":null,"score":{"opus":0.03957442817036712,"gpt":0.3906780638829798,"spread":0.3511036357126127,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008825609,0.0005826498,0.001806595,0.000412211,0.000288307,0.0001043208,0.0002512356,0.0001296473,0.00003532438],"category_scores_gemma":[0.0001673139,0.0004029054,0.0005730616,0.0004275644,0.00006232364,0.0000688936,0.0005163496,0.0006180868,0.00002871773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002711161,"about_ca_system_score_gemma":0.00007465247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004518729,"about_ca_topic_score_gemma":0.00003236044,"domain_scores_codex":[0.9965695,0.0006452928,0.001501655,0.0006624241,0.0003200073,0.0003010992],"domain_scores_gemma":[0.9975386,0.001212789,0.0007416715,0.0003367296,0.00009516952,0.00007506194],"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":[0.00005247461,0.0003506598,0.0003087916,0.0659992,0.0004853396,0.00004845131,0.000839723,0.00004536168,7.543932e-10,0.001578163,0.00469812,0.9255937],"study_design_scores_gemma":[0.001800759,0.000862115,0.001228015,0.08320931,0.007524577,0.00008332606,0.0007756822,0.005688329,7.289158e-9,0.01174225,0.8865789,0.0005067788],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001331255,0.9254298,0.06749626,0.00006123733,0.0002053984,0.005478538,0.001015231,0.0000291449,0.0002710366],"genre_scores_gemma":[0.00003778319,0.9515997,0.04090777,0.00006759683,0.00006538221,0.001550966,0.005439917,0.00005086178,0.0002800585],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9250869,"threshold_uncertainty_score":0.9998423,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2151560129","doi":"10.1002/wics.1269","title":"Sparse matrix computations with application to solve system of nonlinear equations","year":2013,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Lethbridge","funders":"HORIZON EUROPE Excellent Science; Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd","keywords":"Sparse matrix; Matrix-free methods; Nonlinear system; Linear algebra; Computer science; Applied mathematics; Numerical linear algebra; Numerical analysis; Nonlinear programming; Algorithm; Matrix (chemical analysis); Mathematical optimization; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0407346301733776,"gpt":0.3558509177304233,"spread":0.3151162875570457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007456825,0.0007760904,0.002460806,0.0005630318,0.0003833192,0.0002195411,0.001740756,0.0001837233,0.0000393146],"category_scores_gemma":[0.0000804292,0.0006022046,0.0004088244,0.001541793,0.0001478631,0.0003513201,0.001181747,0.0004218764,0.001574149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949691,"about_ca_system_score_gemma":0.0004935019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001236345,"about_ca_topic_score_gemma":0.000006099121,"domain_scores_codex":[0.9950362,0.0004221243,0.002442461,0.0009830311,0.0006839805,0.0004321646],"domain_scores_gemma":[0.9943677,0.001632567,0.001915543,0.001048945,0.0007063262,0.0003289838],"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":[0.000004461045,0.0001569238,2.971255e-7,0.01253731,0.0001562826,0.00001254861,0.0002629414,0.01410121,8.891271e-8,0.2597584,0.002969286,0.7100403],"study_design_scores_gemma":[0.0002667243,0.0004266362,0.000002076747,0.02188112,0.0006613822,0.000221423,0.00006898896,0.5285223,2.414836e-7,0.009433908,0.4375282,0.0009870457],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[1.473558e-7,0.395976,0.6000762,0.00004242727,0.0002091745,0.002522834,0.0009439378,0.0001030625,0.0001262541],"genre_scores_gemma":[0.000005743874,0.2849733,0.7120887,0.0000262542,0.0001755019,0.0009353535,0.001634718,0.00005872883,0.0001017301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7090533,"threshold_uncertainty_score":0.9996429,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408757431","doi":"10.1002/wics.70013","title":"Nuclear Norm Regularization","year":2025,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Regularization (linguistics); Norm (philosophy); Computer science; Applied mathematics; Econometrics; Artificial intelligence; Philosophy; Epistemology","retraction":null,"screen_n_in":null,"score":{"opus":0.0314365038822919,"gpt":0.3316734558952978,"spread":0.3002369520130059,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001659114,0.0006276209,0.001823695,0.0003248822,0.0001881191,0.0001129824,0.0004707313,0.000261994,0.0001197817],"category_scores_gemma":[0.00005640419,0.0005748569,0.0003957553,0.0004723973,0.00007737312,0.00009706629,0.0004660067,0.0004757702,0.0003398095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002472289,"about_ca_system_score_gemma":0.00009464288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.579636e-7,"about_ca_topic_score_gemma":0.00000150853,"domain_scores_codex":[0.9976385,0.0001756857,0.001249805,0.000418485,0.0002410094,0.0002765408],"domain_scores_gemma":[0.9986529,0.0003693727,0.0003103683,0.0004501298,0.0001283069,0.0000889576],"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":[0.000001172183,0.00002014223,7.520299e-8,0.0121019,0.00009661545,0.00001824364,0.00003460388,0.001757732,5.19547e-8,0.00490733,0.1934201,0.7876421],"study_design_scores_gemma":[0.00004890291,0.0000326634,4.992232e-7,0.04694844,0.0004600207,0.00004907108,0.000003785211,0.02474695,9.973819e-8,0.01483237,0.9123861,0.0004911406],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.489886e-8,0.6984627,0.2967864,0.000006895049,0.0005221047,0.0007361958,0.000440611,0.0004138612,0.002631233],"genre_scores_gemma":[9.649546e-7,0.9315031,0.06492647,0.00004099656,0.0001839984,0.00009715599,0.002769761,0.0001039522,0.0003735843],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7871509,"threshold_uncertainty_score":0.9996703,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2008519152","doi":"10.1002/wics.128","title":"Geometry in statistics","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Information geometry; Statistical inference; Nonparametric statistics; Euclidean geometry; Geometry; Computer science; Focus (optics); Inference; Mathematics; Statistics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.06107983404593441,"gpt":0.403007021376157,"spread":0.3419271873302225,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001702212,0.001048136,0.003513369,0.0008325364,0.000238093,0.0003463551,0.002413422,0.0005527977,0.0001241924],"category_scores_gemma":[0.0003193197,0.0008814461,0.0004877987,0.001386232,0.0001848124,0.0003439883,0.002171945,0.002027133,0.0006491124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002845726,"about_ca_system_score_gemma":0.0006687861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005305161,"about_ca_topic_score_gemma":0.00003216048,"domain_scores_codex":[0.9933937,0.001088595,0.002804088,0.00134082,0.0006462937,0.0007265506],"domain_scores_gemma":[0.9948617,0.002045018,0.001340371,0.001196221,0.0002266813,0.0003299971],"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":[0.000001461536,0.0001108295,9.509895e-7,0.005446726,0.00003076635,0.0001454865,0.0001061431,0.00001971387,1.761882e-8,0.1528048,0.02106659,0.8202665],"study_design_scores_gemma":[0.0001519656,0.00008694756,0.00000572016,0.009427024,0.0001380433,0.0002286326,0.000001602175,0.01378528,1.499398e-8,0.1748665,0.8005325,0.0007758615],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[1.289498e-8,0.493122,0.5039703,0.00002710827,0.0008271073,0.0007942276,0.0009824552,0.00004811096,0.0002286703],"genre_scores_gemma":[5.122457e-8,0.5043157,0.4942226,0.00007574501,0.0001515605,0.0001492965,0.0008035981,0.00004832944,0.0002330667],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8194907,"threshold_uncertainty_score":0.9993636,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012094477","doi":"10.1002/wics.1203","title":"The History of ViSta: The Visual Statistics System","year":2012,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Data Analysis with R","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Graphics; Computer science; Software; Computational statistics; Graphics software; Data science; Computer graphics (images); Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.06514940073309329,"gpt":0.3608964615620697,"spread":0.2957470608289764,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002900448,0.0008952474,0.002770918,0.0002351768,0.0006487511,0.0002756889,0.004403908,0.0001865245,0.00004441096],"category_scores_gemma":[0.0002427241,0.0004882743,0.0007185091,0.0007509539,0.0007161982,0.0003558478,0.003360912,0.0008190181,0.0007324965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169537,"about_ca_system_score_gemma":0.00126446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001312904,"about_ca_topic_score_gemma":0.00002424781,"domain_scores_codex":[0.9917203,0.002066528,0.003484547,0.0008117959,0.00124344,0.0006733724],"domain_scores_gemma":[0.9881645,0.005393866,0.003810966,0.001828059,0.0005665851,0.0002360038],"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":[0.000002824927,0.00006858829,7.531056e-7,0.008057545,0.0002658361,0.0000150798,0.0002366371,0.00004847498,8.843341e-9,0.1167269,0.1913972,0.6831802],"study_design_scores_gemma":[0.00008003439,0.00009749903,0.00000572096,0.006453343,0.001379775,0.0001551999,0.00004165623,0.03455646,6.21872e-9,0.002058729,0.9546877,0.0004839273],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.008797e-9,0.5148519,0.4810359,0.00002925197,0.001898602,0.0007597808,0.001169643,0.00004074504,0.0002141563],"genre_scores_gemma":[0.000002818174,0.8962861,0.09996739,0.00004926414,0.0003616154,0.0002971347,0.001767144,0.00007138952,0.001197143],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7632905,"threshold_uncertainty_score":0.9997569,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312140143","doi":"10.1002/wics.1605","title":"A survey of smoothing techniques based on a backfitting algorithm in estimation of semiparametric additive models","year":2022,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Semiparametric regression; Estimator; Smoothing; Additive model; Semiparametric model; Computer science; Nonparametric regression; Kernel smoother; Nonparametric statistics; Smoothing spline; Exploratory data analysis; Statistical model; Econometrics; Generalized additive model; Mathematics; Machine learning; Kernel method; Data mining; Statistics; Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.2464137858525621,"gpt":0.4912400124815751,"spread":0.2448262266290129,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004006881,0.0006767218,0.003926424,0.0009612659,0.0001267961,0.0000210609,0.0005004814,0.0002099316,0.0001527251],"category_scores_gemma":[0.005740625,0.000605479,0.0004074221,0.001548984,0.0001746711,0.000133147,0.0005261041,0.0008842828,0.000003231591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004771417,"about_ca_system_score_gemma":0.0004162335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003144885,"about_ca_topic_score_gemma":0.000009923086,"domain_scores_codex":[0.9916407,0.002825548,0.003661302,0.0007193216,0.000809829,0.0003432999],"domain_scores_gemma":[0.9643787,0.03159964,0.003149487,0.0004649927,0.0003076579,0.00009949222],"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":[0.00002185623,0.0004143008,4.048012e-7,0.01478886,0.00004762738,0.00001621385,0.0001473777,0.0301382,9.00456e-9,0.01090003,0.0007925058,0.9427326],"study_design_scores_gemma":[0.0001656714,0.0003999132,0.000002712545,0.02706008,0.0002585587,0.000008083939,0.00001766082,0.7057015,2.180471e-7,0.2601693,0.005806464,0.00040978],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[1.666727e-7,0.3043748,0.6831134,0.000002459846,0.00007985006,0.001573998,0.01065812,0.00002444258,0.0001727852],"genre_scores_gemma":[0.000006764071,0.402179,0.594798,0.00001061427,0.0000145927,0.0003734476,0.002541387,0.00006764002,0.00000848545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9423229,"threshold_uncertainty_score":0.9996396,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1975347971","doi":"10.1002/wics.173","title":"Statistics of shape","year":2011,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Landmark; Shape analysis (program analysis); Representation (politics); Computer science; Statistical analysis; Statistics; Artificial intelligence; Mathematics; Pattern recognition (psychology); Static analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.1754938258053004,"gpt":0.4213681988902794,"spread":0.2458743730849789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008824413,0.0008342792,0.003908749,0.0003296786,0.0001993482,0.00004441513,0.0008124985,0.0003575718,0.003279778],"category_scores_gemma":[0.0007951246,0.0006299967,0.0006691935,0.0005629545,0.0002669617,0.00008752781,0.0009534366,0.0006567009,0.0005583757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001451704,"about_ca_system_score_gemma":0.0002737554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003757945,"about_ca_topic_score_gemma":0.000003742307,"domain_scores_codex":[0.9943267,0.0006862555,0.003442612,0.0006850117,0.0004433751,0.0004160443],"domain_scores_gemma":[0.9925366,0.003396455,0.002816478,0.0006597161,0.0003886633,0.0002020581],"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":[0.000004456446,0.0002680106,0.000001205399,0.01829487,0.0001584901,0.00002257506,0.00005790785,0.000002637107,1.041293e-8,0.2633162,0.1241588,0.5937149],"study_design_scores_gemma":[0.0001274547,0.0002045418,0.000005237208,0.01345526,0.001160073,0.00008332718,0.00001155439,0.0008366724,2.11717e-8,0.2750657,0.7085184,0.0005317462],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.063788e-7,0.5639993,0.4271559,0.000003894398,0.0002705128,0.0009053012,0.006799541,0.00003426639,0.0008312116],"genre_scores_gemma":[5.271741e-7,0.5703048,0.4271763,0.00001585001,0.0001096791,0.0001198012,0.001931099,0.00006667191,0.0002752496],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5931832,"threshold_uncertainty_score":0.9996151,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3098230082","doi":"10.1002/wics.1536","title":"A convergence diagnostic for Bayesian clustering","year":2020,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","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":"Huawei Technologies (Canada); McGill University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Cluster analysis; Computer science; Gibbs sampling; Posterior probability; Bayesian probability; Data mining; Markov chain; Machine learning; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.06919171479297738,"gpt":0.3906654161054129,"spread":0.3214737013124356,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000796949,0.0009417115,0.003204216,0.0002115126,0.0003824976,0.0003148908,0.002074437,0.0002555966,0.00004988286],"category_scores_gemma":[0.0007588724,0.0007997355,0.0009060996,0.0006402902,0.00011061,0.000297618,0.002010345,0.00056278,0.0002599303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002303116,"about_ca_system_score_gemma":0.0005094734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001314321,"about_ca_topic_score_gemma":0.000003998739,"domain_scores_codex":[0.9948328,0.000710114,0.002111877,0.001357289,0.0004097782,0.0005781387],"domain_scores_gemma":[0.9925461,0.004894238,0.00119269,0.0007421148,0.0002183118,0.0004065035],"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":[0.000003099065,0.00004335718,1.52178e-7,0.01546144,0.00009560206,0.00006837393,0.000198483,0.00007357789,1.268301e-8,0.04609177,0.03188846,0.9060757],"study_design_scores_gemma":[0.000136411,0.0001768885,3.450247e-7,0.01258228,0.000318721,0.0001607987,0.000001952512,0.1769456,1.864633e-8,0.08171491,0.7273127,0.0006492925],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[5.30216e-10,0.4932404,0.5034195,0.0001234303,0.0007455313,0.001684675,0.0006330413,0.00007720043,0.00007631805],"genre_scores_gemma":[1.5231e-7,0.5069298,0.4914987,0.0001520121,0.0002601663,0.0005946432,0.0004501007,0.00005094941,0.00006351413],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9054264,"threshold_uncertainty_score":0.9994454,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2091793380","doi":"10.1002/wics.102","title":"Bayesian inference: an approach to statistical inference","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Prior probability; Bayes' theorem; Statistical inference; Frequentist inference; Bayesian probability; Inference; Bayes factor; Mathematics; Computer science; Bayesian inference; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1451709663558175,"gpt":0.485227297478791,"spread":0.3400563311229735,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.002021801,0.00177241,0.005582494,0.0005862416,0.0005832901,0.0005183254,0.001864413,0.0008300523,0.001044031],"category_scores_gemma":[0.007066633,0.001416651,0.0005294152,0.001008519,0.0005321541,0.0002842129,0.001505919,0.002538005,0.0008317214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003133772,"about_ca_system_score_gemma":0.0009521919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001123806,"about_ca_topic_score_gemma":0.000023946,"domain_scores_codex":[0.9895834,0.002134058,0.004002559,0.001984302,0.001168137,0.001127485],"domain_scores_gemma":[0.9823007,0.01278914,0.001483133,0.001489496,0.0005910106,0.001346539],"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":[0.00001008516,0.0004150457,0.000001854618,0.00914708,0.00005942795,0.00002846036,0.0001903773,0.000010699,4.403043e-8,0.4099125,0.007683801,0.5725406],"study_design_scores_gemma":[0.0001464102,0.0003523456,0.0000094093,0.008988959,0.000767627,0.0001145163,0.00002708082,0.007703902,2.245885e-8,0.613068,0.3675359,0.001285767],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[8.55984e-8,0.2874609,0.7009503,0.0000168702,0.0005735962,0.002430604,0.006726531,0.0001428272,0.001698258],"genre_scores_gemma":[0.000002041046,0.3994339,0.5962071,0.0000865991,0.0003785997,0.000730977,0.002921171,0.0001530926,0.00008651165],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5712549,"threshold_uncertainty_score":0.9999462,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413897197","doi":"10.1002/wics.70042","title":"A Systematic Categorization of Performance Measures for Estimated Non‐Linear Associations Between an Outcome and Continuous Predictors","year":2025,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Austrian Science Fund; Deutsche Forschungsgemeinschaft","keywords":"Outcome (game theory); Categorization; Econometrics; Linear model; Statistics; Mathematics; Computer science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.2606072769033529,"gpt":0.4662352797980702,"spread":0.2056280028947173,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007364552,0.0002231536,0.00174894,0.0003139885,0.0003222508,0.00005729823,0.0002220597,0.00010848,0.00001517661],"category_scores_gemma":[0.003490971,0.0002466757,0.00009986989,0.0002479677,0.0000913,0.00033385,0.0001009928,0.0001130049,0.00004138048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002922572,"about_ca_system_score_gemma":0.00011895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000299135,"about_ca_topic_score_gemma":0.0000242687,"domain_scores_codex":[0.9933752,0.0003168632,0.005562271,0.000404482,0.0001172871,0.0002238711],"domain_scores_gemma":[0.9941643,0.002133073,0.002984115,0.0002578495,0.0003566836,0.0001039912],"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":[0.00004032597,0.0003024341,0.7622573,0.05386011,0.0006351498,5.013653e-7,0.004217352,0.01785934,0.000002382446,0.1484771,0.01028056,0.002067484],"study_design_scores_gemma":[0.001176556,0.0004250824,0.2385927,0.00550212,0.0002356167,0.000002690282,0.0004128312,0.702414,0.000001688052,0.05014426,0.0006324649,0.0004599564],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03187023,0.002418851,0.9568948,0.0009197809,0.0003411768,0.002638285,0.004751578,0.0000362776,0.0001290405],"genre_scores_gemma":[0.8811071,0.0002992521,0.1141155,0.0005225971,0.0001462312,0.0004936142,0.003040414,0.00003802497,0.0002372502],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8492368,"threshold_uncertainty_score":0.9999986,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}