{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":71,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":71,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"d5798dda5751","filters":{"venue":"The Annals of Applied Statistics"}},"results":[{"id":"W2126292488","doi":"10.1214/09-aoas285","title":"BART: Bayesian additive regression trees","year":2010,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1515,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada; Isaac Newton Institute for Mathematical Sciences; National Science Foundation","keywords":"Frequentist inference; Bayesian probability; Nonparametric regression; Bayesian inference; Bayesian linear regression; Markov chain Monte Carlo; Boosting (machine learning); Feature selection; Inference; Bayesian average","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.130137471622647,"gpt":0.4114514803502196,"spread":0.2813140087275725,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005858183,0.001179962,0.001838076,0.001770505,0.0008235721,0.002335827,0.003989236,0.002449549,0.006829626],"category_scores_gemma":[0.01747052,0.00119812,0.001647466,0.002581868,0.001053596,0.002926639,0.002792375,0.004306383,0.005979211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009243852,"about_ca_system_score_gemma":0.001507071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003550155,"about_ca_topic_score_gemma":0.004721936,"domain_scores_codex":[0.9961551,0.002064959,0.0001174131,0.0003684404,0.001085178,0.0002089366],"domain_scores_gemma":[0.9948836,0.003257942,0.0004008558,0.0005510261,0.0006779949,0.0002284798],"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":[0.0002017505,0.0001173054,0.001706639,0.0002802826,0.0002515056,0.0001575882,0.0002067004,0.3565176,0.001590378,0.347769,0.03289928,0.2583019],"study_design_scores_gemma":[0.00002540653,0.00003496357,0.0002338614,0.00004340921,0.00003175262,0.00008617915,0.00001026971,0.8395257,0.0004363348,0.1437352,0.01580663,0.00003019958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001108729,0.0004862959,0.995223,0.0003818639,0.00008228606,0.00002868462,0.0002562092,0.0008406941,0.001592319],"genre_scores_gemma":[0.09928289,0.001968164,0.8833504,0.00108399,0.0005087338,0.0004891581,0.001789109,0.0009684127,0.01055912],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006829626,"threshold_uncertainty_score":0.03098142,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971541813","doi":"10.1214/10-aoas442","title":"Forecasting emergency medical service call arrival rates","year":2011,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":123,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Queueing theory; Context (archaeology); Exponential smoothing; Smoothing; Covariate; Econometrics; Mathematics; Machine learning; Geography","authors":[{"name":"David S. Matteson","is_ca":false},{"name":"Mathew W. McLean","is_ca":false},{"name":"Dawn B. Woodard","is_ca":false},{"name":"Shane G. Henderson","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5015733237736453,"gpt":0.4569802608908218,"spread":0.0445930628828235,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001964509,0.0004879364,0.0008508734,0.0008012544,0.0001817869,0.0008239662,0.001049524,0.0007315808,0.0006142866],"category_scores_gemma":[0.009385795,0.0003585518,0.0005127943,0.0009343312,0.0002902829,0.001034223,0.0004978348,0.0010483,0.0001662924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008643653,"about_ca_system_score_gemma":0.001041293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01275745,"about_ca_topic_score_gemma":0.0099111,"domain_scores_codex":[0.9991116,0.0003878467,0.0000468551,0.0001392794,0.0002552089,0.00005917321],"domain_scores_gemma":[0.9970796,0.001789017,0.0004030742,0.0002777434,0.0003680915,0.00008245379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005524134,0.00005730058,0.005007194,0.00002168696,0.00005161709,0.00002815286,0.00002946753,0.9527652,0.001277152,0.005740112,0.0004424528,0.0345244],"study_design_scores_gemma":[0.000002922982,0.000006696352,0.0003270551,0.000001235583,0.000002743689,0.000003318217,0.000001869506,0.998588,0.0001328987,0.0008171121,0.000112984,0.000003190675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08338574,0.0001768225,0.9144583,0.0002686071,0.00006651613,0.00003101334,0.0002216904,0.0004527253,0.0009384456],"genre_scores_gemma":[0.7775754,0.0002603489,0.2203349,0.00005833475,0.00009176853,0.00006247038,0.0005334692,0.00004933085,0.001034068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01275745,"threshold_uncertainty_score":0.02536637,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098683668","doi":"10.1214/08-aoas222","title":"Handbook for the GREAT08 Challenge: An image analysis competition for cosmological lensing","year":2009,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":121,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria; University of British Columbia","funders":"Science and Technology Facilities Council; European Commission; National Aeronautics and Space Administration; University College London; California Institute of Technology; Jet Propulsion Laboratory","keywords":"Weak gravitational lensing; Dark energy; Dark matter; Galaxy; Inference; Strong gravitational lensing; Cosmology; Gravitational lens; Physics; Data science; Astrophysics; Computer science; Theoretical physics; Astronomy; Artificial intelligence; Redshift","authors":[{"name":"Sarah Bridle","is_ca":false},{"name":"Mandeep Gill","is_ca":false},{"name":"Alan Heavens","is_ca":false},{"name":"Catherine Heymans","is_ca":true},{"name":"F. William High","is_ca":false},{"name":"Henk Hoekstra","is_ca":true},{"name":"Mike Jarvis","is_ca":false},{"name":"Donnacha Kirk","is_ca":false},{"name":"Thomas Kitching","is_ca":false},{"name":"Jean‐Paul Kneib","is_ca":false},{"name":"Konrad Kuijken","is_ca":false},{"name":"John Shawe‐Taylor","is_ca":false},{"name":"David Lagatutta","is_ca":false},{"name":"Rachel Mandelbaum","is_ca":false},{"name":"R. Massey","is_ca":false},{"name":"Y. Mellier","is_ca":false},{"name":"Baback Moghaddam","is_ca":false},{"name":"Y. Moudden","is_ca":false},{"name":"Reiko Nakajima","is_ca":false},{"name":"Stephane Paulin-Henriksson","is_ca":false},{"name":"Sandrine Pires","is_ca":false},{"name":"A. Rassat","is_ca":false},{"name":"A. Amara","is_ca":false},{"name":"Alexandre Réfrégier","is_ca":false},{"name":"Jason Rhodes","is_ca":false},{"name":"T. Schrabback","is_ca":false},{"name":"E. Semboloni","is_ca":false},{"name":"Marina Shmakova","is_ca":false},{"name":"Ludovic Van Waerbeke","is_ca":true},{"name":"D. K. Witherick","is_ca":false},{"name":"Lisa M Voigt","is_ca":false},{"name":"David Wittman","is_ca":false},{"name":"Douglas Applegate","is_ca":false},{"name":"S. T. Balan","is_ca":false},{"name":"Joel Bergé","is_ca":false},{"name":"G. M. Bernstein","is_ca":false},{"name":"Håkon Dahle","is_ca":false},{"name":"Thomas Erben","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05355674815407757,"gpt":0.3134809803572959,"spread":0.2599242322032183,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01957906,0.002541239,0.003307592,0.005091679,0.002589499,0.0100818,0.005792978,0.005605881,0.1167934],"category_scores_gemma":[0.04411716,0.001420325,0.001882543,0.00556711,0.001896627,0.01053015,0.008729979,0.006309089,0.1079672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003248421,"about_ca_system_score_gemma":0.00681085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01315992,"about_ca_topic_score_gemma":0.01926396,"domain_scores_codex":[0.987038,0.002619624,0.0008171785,0.00141203,0.007301448,0.0008118002],"domain_scores_gemma":[0.9669117,0.009538485,0.0006318045,0.005148883,0.0128002,0.004968989],"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":[0.00002769948,0.00001817587,0.00005897975,0.00006556272,0.000008793962,0.00001559888,0.00001627614,0.0002779693,0.0001308279,0.004164896,0.9501196,0.04509558],"study_design_scores_gemma":[0.00005307887,0.00003755208,0.00056034,0.0001118811,0.000008430893,0.0001761946,0.00006564137,0.007934963,0.0003583345,0.02675904,0.9638863,0.00004820568],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004148121,0.0390286,0.406853,0.1524413,0.03661472,0.001880811,0.04366672,0.06288806,0.2524787],"genre_scores_gemma":[0.03353108,0.02061752,0.3751352,0.0201778,0.02401688,0.002610619,0.1029614,0.0305955,0.3903541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1167934,"threshold_uncertainty_score":0.390713,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2088906176","doi":"10.1214/13-aoas626","title":"Travel time estimation for ambulances using Bayesian data augmentation","year":2013,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":109,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Global Positioning System; Bayesian probability; Computer science; Estimation; Path (computing); Data mining; Statistics; Mathematics; Artificial intelligence; Engineering","authors":[{"name":"Bradford S. Westgate","is_ca":false},{"name":"Dawn B. Woodard","is_ca":false},{"name":"David S. Matteson","is_ca":false},{"name":"Shane G. Henderson","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.144214332618472,"gpt":0.3997035024737231,"spread":0.2554891698552512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003106464,0.0007981189,0.001489606,0.00162453,0.0005151264,0.001130005,0.002742977,0.001360513,0.002092088],"category_scores_gemma":[0.02079153,0.001121944,0.001232306,0.002127016,0.0007502825,0.00274697,0.001574004,0.002289332,0.0006763603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001209235,"about_ca_system_score_gemma":0.00177619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02168693,"about_ca_topic_score_gemma":0.02045107,"domain_scores_codex":[0.9978251,0.001147397,0.00009844352,0.0003589148,0.000464394,0.0001057282],"domain_scores_gemma":[0.9926692,0.004686665,0.0007757705,0.0009430746,0.0007798451,0.0001453339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001004587,0.00007947709,0.00498691,0.00007272308,0.00009332896,0.00004566598,0.0001053468,0.9272624,0.0007815151,0.01678413,0.001666253,0.04802182],"study_design_scores_gemma":[0.00000996061,0.00001253322,0.0008149136,0.00001088196,0.000007495489,0.00001687263,0.000007506254,0.9894401,0.0002092568,0.008900558,0.0005539518,0.00001605717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02155816,0.0001532271,0.9767857,0.0001771823,0.00001869487,0.00003963196,0.0003838831,0.0003042557,0.0005792917],"genre_scores_gemma":[0.5093307,0.000540765,0.4841322,0.0001848762,0.0001077006,0.0005107994,0.00324572,0.0002253633,0.00172189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02168693,"threshold_uncertainty_score":0.04312134,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2891725784","doi":"10.1214/17-aoas1119","title":"Topological data analysis of single-trial electroencephalographic signals","year":2018,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; King Abdullah University of Science and Technology; McMaster University; University of Wisconsin-Madison; Emory University; National Institutes of Health; National Science Foundation","keywords":"Electroencephalography; Pattern recognition (psychology); Computer science; Resampling; Neurophysiology; Artificial intelligence; Speech recognition; Neuroscience; Psychology","authors":[{"name":"Yuan Wang","is_ca":false},{"name":"Hernando Ombao","is_ca":false},{"name":"Moo K. Chung","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1682789064168544,"gpt":0.3625785741721692,"spread":0.1942996677553148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263344,0.0003946574,0.0004584132,0.002723515,0.0003294987,0.0007010206,0.0005294388,0.0003891588,0.001413538],"category_scores_gemma":[0.01053691,0.0001114196,0.0005785851,0.001517828,0.0007629335,0.001056372,0.0008638753,0.0004748758,0.0002295505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003047036,"about_ca_system_score_gemma":0.0003970615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006619696,"about_ca_topic_score_gemma":0.0007108211,"domain_scores_codex":[0.9992362,0.0002520308,0.00007027017,0.0001696789,0.0001971255,0.00007469956],"domain_scores_gemma":[0.995306,0.002632003,0.0006619441,0.0006740516,0.0005546258,0.0001714321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001594899,0.0003283516,0.06515437,0.001118413,0.0006952084,0.001109108,0.001081716,0.2287464,0.1105117,0.0451021,0.003923007,0.5406347],"study_design_scores_gemma":[0.00002977587,0.0006138701,0.06914616,0.00004181285,0.0001092868,0.0008303504,0.0004757445,0.8630608,0.02288131,0.03917649,0.003522812,0.0001116509],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5351376,0.000349359,0.4607028,0.0001883731,0.00005241637,0.0001002511,0.001295058,0.0006533746,0.001520774],"genre_scores_gemma":[0.9443266,0.0001535853,0.05334714,0.0000212582,0.00002893613,0.00008413202,0.001683276,0.00005059438,0.0003045557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002723515,"threshold_uncertainty_score":0.006681323,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2790880830","doi":"10.1214/17-aoas1092","title":"Automated threshold selection for extreme value analysis via ordered goodness-of-fit tests with adjustment for false discovery rate","year":2018,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Generalized Pareto distribution; Goodness of fit; False discovery rate; Statistics; Extreme value theory; Sample size determination; Mathematics; Selection (genetic algorithm); Multiple comparisons problem; Threshold limit value; Computer science; Model selection; Artificial intelligence","authors":[{"name":"B.E. Bader","is_ca":true},{"name":"Jun Yan","is_ca":true},{"name":"Xuebin Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05727779193429081,"gpt":0.3207032615150507,"spread":0.2634254695807599,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08926175,0.001608701,0.003516146,0.005204268,0.001668199,0.003701739,0.004938726,0.002433501,0.003782209],"category_scores_gemma":[0.2615073,0.0009563544,0.002744787,0.00487991,0.003173508,0.002721689,0.003015887,0.004944191,0.001689113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001246824,"about_ca_system_score_gemma":0.003852857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265269,"about_ca_topic_score_gemma":0.001755897,"domain_scores_codex":[0.9341075,0.04547247,0.004991998,0.005921488,0.00851771,0.0009887932],"domain_scores_gemma":[0.7391957,0.2081477,0.01343174,0.02530318,0.01274586,0.001175836],"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":[0.001950684,0.0008791346,0.06103146,0.002016311,0.002497268,0.001972268,0.002158135,0.07691275,0.02888037,0.07263017,0.01821006,0.7308614],"study_design_scores_gemma":[0.0005429525,0.001403499,0.02955346,0.0003524947,0.000478299,0.001374303,0.0003351172,0.756821,0.0312556,0.162296,0.01513519,0.0004522182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009392961,0.0001481597,0.9875253,0.0001303849,0.00008568932,0.0003517765,0.0001811524,0.001693833,0.0004906249],"genre_scores_gemma":[0.1344594,0.00009861619,0.8619163,0.0002038424,0.00006804844,0.001865977,0.0005324013,0.0005316762,0.0003237174],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08926175,"threshold_uncertainty_score":0.4720669,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057931901","doi":"10.1214/13-aoas684","title":"Beta regression for time series analysis of bounded data, with application to Canada Google® Flu Trends","year":2014,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Bounded function; Autoregressive model; Mathematics; Econometrics; Statistics; Autoregressive integrated moving average; Regression analysis; Time series; Computer science","authors":[{"name":"Annamaria Guolo","is_ca":false},{"name":"Cristiano Varin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1102783874309793,"gpt":0.4260513457302483,"spread":0.315772958299269,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007746878,0.0009594493,0.0008822595,0.001527643,0.0005745802,0.001306939,0.001032259,0.0009647902,0.003795341],"category_scores_gemma":[0.0326163,0.0004327769,0.001047609,0.003010197,0.0007606304,0.000913324,0.001519403,0.00243328,0.001278424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037869,"about_ca_system_score_gemma":0.002361706,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01693557,"about_ca_topic_score_gemma":0.01531341,"domain_scores_codex":[0.9966085,0.002265234,0.0001321463,0.0003384178,0.0005504232,0.0001053633],"domain_scores_gemma":[0.9875979,0.009781452,0.0008622615,0.0006660422,0.0009466423,0.0001456484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001773165,0.0001560403,0.01082969,0.000434134,0.0004243888,0.0008449104,0.000736502,0.3285053,0.004307956,0.3604489,0.01773018,0.2754048],"study_design_scores_gemma":[0.00001596517,0.00005388719,0.002344193,0.00005687487,0.00002355985,0.0001113347,0.00008908397,0.9253388,0.0006206019,0.06192989,0.009378963,0.00003677185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004531863,0.000544609,0.9927931,0.0003696395,0.00005746063,0.00004079507,0.0002547464,0.0004551837,0.0009526491],"genre_scores_gemma":[0.2188949,0.003059929,0.7688921,0.0003273403,0.0003039922,0.0008629119,0.001376562,0.0006832447,0.005599039],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9830644,"threshold_uncertainty_score":0.04096991,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1970646576","doi":"10.1214/14-aoas727","title":"Effect of breastfeeding on gastrointestinal infection in infants: A targeted maximum likelihood approach for clustered longitudinal data","year":2014,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Université de Montréal","funders":"National Institute of Allergy and Infectious Diseases","keywords":"Breastfeeding; Confounding; Breastfeeding promotion; Context (archaeology); Estimation; Duration (music); Intervention (counseling); Random effects model; Breast feeding","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.1072896563077069,"gpt":0.3946682202134613,"spread":0.2873785639057544,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0414477,0.001035936,0.002001331,0.001675377,0.0006879593,0.00124376,0.002661648,0.002021703,0.001795547],"category_scores_gemma":[0.1096079,0.0008669721,0.003145837,0.001256725,0.001403033,0.001202412,0.002489639,0.002330331,0.0001699108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382568,"about_ca_system_score_gemma":0.002062255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0045161,"about_ca_topic_score_gemma":0.004033878,"domain_scores_codex":[0.9626595,0.03441446,0.0004760882,0.001506998,0.0006737583,0.0002691628],"domain_scores_gemma":[0.8911803,0.1001929,0.00377375,0.003275856,0.001133097,0.0004440836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.004118247,0.0009275773,0.04145105,0.002295841,0.006426323,0.0007151958,0.001392825,0.5474221,0.00228819,0.1492365,0.002807466,0.2409187],"study_design_scores_gemma":[0.0004477698,0.0005678952,0.00522893,0.0001592131,0.0006313,0.0001004325,0.00009525099,0.919386,0.0008523506,0.071371,0.001112538,0.00004722629],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0552473,0.00144842,0.9405357,0.001387246,0.00005388005,0.0003143915,0.0002463455,0.0002659401,0.00050081],"genre_scores_gemma":[0.5162977,0.001152122,0.478884,0.0006305516,0.0001135321,0.001205381,0.0005516768,0.0001131245,0.001051895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0414477,"threshold_uncertainty_score":0.219199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3191733181","doi":"10.1214/21-aoas1501","title":"The ASA president’s task force statement on statistical significance and replicability","year":2021,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":41,"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":"Task force; Statement (logic); Task (project management); Statistical analysis; Statistics; Computer science; Econometrics; Natural language processing; Mathematics; Political science; Law; Management; Economics","authors":[{"name":"Yoav Benjamini","is_ca":false},{"name":"Richard D. De Veaux","is_ca":false},{"name":"Bradley Efron","is_ca":false},{"name":"Scott Evans","is_ca":false},{"name":"Mark E. Glickman","is_ca":false},{"name":"Barry I. Graubard","is_ca":false},{"name":"Xuming He","is_ca":false},{"name":"Xiao‐Li Meng","is_ca":false},{"name":"Nancy Reid","is_ca":true},{"name":"Stephen M. Stigler","is_ca":false},{"name":"Stephen B. Vardeman","is_ca":false},{"name":"Christopher K. Wikle","is_ca":false},{"name":"Tommy Wright","is_ca":false},{"name":"Linda J. Young","is_ca":false},{"name":"Karen Kafadar","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5158428041741353,"gpt":0.5546070747217429,"spread":0.03876427054760756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.484811,0.003085638,0.007781122,0.0102478,0.006377546,0.01495982,0.006354643,0.03167778,0.013417],"category_scores_gemma":[0.6991994,0.004084573,0.007631079,0.008229179,0.01879502,0.007347844,0.005701226,0.04422818,0.01791636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005011111,"about_ca_system_score_gemma":0.03574704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004157059,"about_ca_topic_score_gemma":0.00358238,"domain_scores_codex":[0.5791425,0.2494576,0.06759895,0.01248153,0.08648077,0.004838741],"domain_scores_gemma":[0.2028281,0.5724922,0.03759834,0.04958428,0.1305818,0.006915425],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008702278,0.0001478955,0.001190621,0.003292616,0.0005211459,0.0003518421,0.001135021,0.000942177,0.0007009227,0.06591138,0.8377728,0.08716337],"study_design_scores_gemma":[0.0008507982,0.0004758571,0.003277926,0.01400647,0.0006347808,0.0009325843,0.0003367231,0.003558584,0.001412286,0.133505,0.8405852,0.0004237921],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001536615,0.04168119,0.1840216,0.4694703,0.243812,0.008840113,0.004882128,0.001610494,0.04414561],"genre_scores_gemma":[0.02967382,0.04135133,0.3384998,0.3521362,0.1728573,0.03843954,0.00378202,0.002105745,0.02115425],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.5151891,"threshold_uncertainty_score":0.6353198,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2962800545","doi":"10.1214/16-aoas993","title":"A continuous-time stochastic block model for basketball networks","year":2017,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Acadia University; University of Waterloo; Royal Bank of Canada","funders":"","keywords":"Basketball; Analytics; Computer science; Stochastic block model; Data science; Offensive; Block (permutation group theory); Cluster analysis; Machine learning; Artificial intelligence; Operations research; Engineering; Geography; Mathematics","authors":[{"name":"Xin Lü","is_ca":true},{"name":"Mu Zhu","is_ca":true},{"name":"Hugh Chipman","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09067351114725328,"gpt":0.287761093136061,"spread":0.1970875819888077,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002459159,0.001945377,0.002261447,0.00130726,0.0009465429,0.002799259,0.003968029,0.002714006,0.01482789],"category_scores_gemma":[0.006985007,0.00114412,0.00164966,0.001558296,0.00188691,0.002800355,0.001876611,0.003306091,0.002796927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002952008,"about_ca_system_score_gemma":0.001818602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04532421,"about_ca_topic_score_gemma":0.02856896,"domain_scores_codex":[0.9988268,0.0003860407,0.00003745214,0.0003310394,0.0001602416,0.000258314],"domain_scores_gemma":[0.9965952,0.002022437,0.0005159954,0.0001181258,0.0004345069,0.000313771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001563726,0.00006059271,0.002229649,0.0001107923,0.00007096279,0.0001836437,0.0001367958,0.8541661,0.000806345,0.1314551,0.004206965,0.006416624],"study_design_scores_gemma":[0.00002516395,0.00002410991,0.0003042367,0.00001584805,0.0000213245,0.00002309496,0.00002302142,0.9789962,0.00005373211,0.01929663,0.001200152,0.0000166487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05122343,0.001667429,0.9280092,0.002298883,0.0003723957,0.000229064,0.003586382,0.0005230505,0.01209009],"genre_scores_gemma":[0.8569151,0.004400858,0.06083363,0.0007361009,0.0005141386,0.001172291,0.004919325,0.0003349663,0.07017364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04532421,"threshold_uncertainty_score":0.09012079,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2076514995","doi":"10.1214/12-aoas584","title":"Robust VIF regression with application to variable selection in large data sets","year":2013,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Outlier; Robustness (evolution); Feature selection; Computer science; Estimator; Regression; Robust regression; Lasso (programming language); Covariate; Regression analysis; Variance (accounting); Variance inflation factor; Econometrics; Artificial intelligence; Machine learning; Statistics; Mathematics; Multicollinearity; Economics","authors":[{"name":"Debbie J. Dupuis","is_ca":true},{"name":"Maria‐Pia Victoria‐Feser","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2047160822476765,"gpt":0.4366883251623959,"spread":0.2319722429147194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02301944,0.002101356,0.002973266,0.003638728,0.001070633,0.001790844,0.002973313,0.002440035,0.002830846],"category_scores_gemma":[0.08605167,0.001040685,0.002712406,0.004892746,0.001671371,0.001698873,0.002935951,0.004473626,0.001225168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076976,"about_ca_system_score_gemma":0.002759867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00813426,"about_ca_topic_score_gemma":0.006631867,"domain_scores_codex":[0.9869788,0.009645884,0.0004703641,0.001086714,0.001517836,0.0003003939],"domain_scores_gemma":[0.95035,0.0417905,0.001866852,0.003241047,0.002469741,0.0002819875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001871393,0.0001229979,0.005550471,0.0005405953,0.0008088162,0.000552516,0.0003157363,0.5704059,0.001935376,0.1141637,0.008422008,0.2969947],"study_design_scores_gemma":[0.00002971961,0.00004770424,0.0007322481,0.00005094403,0.00003099548,0.00009466768,0.00002372389,0.93725,0.0006570885,0.05667783,0.004369946,0.00003503399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001570804,0.0005687797,0.9968672,0.0002159997,0.00004556744,0.00004410466,0.00007655106,0.0003852376,0.0002256541],"genre_scores_gemma":[0.06811411,0.001331807,0.9270338,0.0002040263,0.0002884686,0.0005853163,0.0006604818,0.0004564355,0.001325622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02301944,"threshold_uncertainty_score":0.1217399,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2767505813","doi":"10.1214/21-aoas1471","title":"Improving exoplanet detection power: Multivariate Gaussian process models for stellar activity","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Astrophysics Division; Pennsylvania Space Grant Consortium; Natural Sciences and Engineering Research Council of Canada; European Commission; National Aeronautics and Space Administration; University of Pennsylvania; Simons Foundation Autism Research Initiative; Pennsylvania State University; Simons Foundation; Institute for Computational and Data Sciences, Pennsylvania State University; Division of Mathematical Sciences; National Science Foundation","keywords":"Exoplanet; Radial velocity; Physics; Planet; Astrophysics; Astronomy; Astron; Gaussian process; Gaussian; Stars","authors":[{"name":"David Jones","is_ca":false},{"name":"David C. Stenning","is_ca":true},{"name":"Eric B. Ford","is_ca":false},{"name":"Robert L. Wolpert","is_ca":false},{"name":"Thomas J. Loredo","is_ca":false},{"name":"Christian Gilbertson","is_ca":false},{"name":"X. Dumusque","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05106795244628423,"gpt":0.325273856738226,"spread":0.2742059042919418,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004478461,0.001626272,0.00168298,0.001608055,0.0005955506,0.001768864,0.002710437,0.001617335,0.001628924],"category_scores_gemma":[0.01248278,0.0007886393,0.002314986,0.001511357,0.001178064,0.001870603,0.001764292,0.003838531,0.0007107361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113638,"about_ca_system_score_gemma":0.001052518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01435785,"about_ca_topic_score_gemma":0.009155998,"domain_scores_codex":[0.9987032,0.0005744547,0.00006023544,0.0003260542,0.0001918436,0.0001442312],"domain_scores_gemma":[0.9940186,0.004106997,0.0006902198,0.0005108749,0.0005046708,0.00016871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000215238,0.0002061916,0.01251649,0.0001061456,0.0003971265,0.0001750523,0.0002862125,0.8536628,0.001552172,0.04947997,0.003155842,0.07824671],"study_design_scores_gemma":[0.000007971167,0.00001078068,0.0005945447,0.00000563576,0.00001534317,0.00001261795,0.000004642637,0.989902,0.00007560582,0.009044407,0.0003153155,0.00001114536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03742104,0.0009188589,0.9587833,0.0007307088,0.00009036652,0.00005182467,0.0003845392,0.0005909731,0.001028439],"genre_scores_gemma":[0.7857145,0.002113982,0.2030461,0.0005539725,0.0006135321,0.0002417283,0.001783405,0.0003117405,0.005621055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01435785,"threshold_uncertainty_score":0.02854854,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2990681736","doi":"10.1214/19-aoas1269","title":"Robust elastic net estimators for variable selection and identification of proteomic biomarkers","year":2019,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Genome Canada","keywords":"Estimator; Outlier; Robustness (evolution); Feature selection; Computer science; Robust statistics; Robust regression; Elastic net regularization; Lasso (programming language); Mathematics; Statistics; Artificial intelligence; Biology","authors":[{"name":"Gabriela V. Cohen Freue","is_ca":true},{"name":"David Kepplinger","is_ca":true},{"name":"Matías Salibián‐Barrera","is_ca":true},{"name":"Ezequiel Smucler","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1225059196152924,"gpt":0.3935837860991451,"spread":0.2710778664838527,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009866947,0.001539614,0.001347471,0.002016509,0.0004999742,0.001077267,0.001886271,0.001426351,0.001777369],"category_scores_gemma":[0.02821338,0.0007277299,0.001127837,0.001359939,0.001524598,0.001805266,0.001829161,0.002389453,0.0005583895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008064747,"about_ca_system_score_gemma":0.001127477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001635224,"about_ca_topic_score_gemma":0.00128829,"domain_scores_codex":[0.9974319,0.001631794,0.0001183093,0.0003267002,0.0003876983,0.0001035333],"domain_scores_gemma":[0.9854005,0.01140763,0.001257892,0.0007924785,0.0009731711,0.0001682555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002059577,0.00009523383,0.002611857,0.0001701982,0.0002144672,0.0001160484,0.0000554684,0.8771178,0.001956559,0.04198239,0.001917818,0.07355618],"study_design_scores_gemma":[0.00001060034,0.00002202915,0.0002337795,0.00001143811,0.000008350685,0.00001681483,0.000004379577,0.984547,0.0004303361,0.01430191,0.0004023545,0.00001105841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003381243,0.0001532753,0.9960458,0.00008895349,0.00001753667,0.00002316543,0.00003939555,0.0001010493,0.0001496316],"genre_scores_gemma":[0.2674382,0.0009144762,0.7250562,0.000350753,0.0002606103,0.000788148,0.001034785,0.0003161257,0.003840634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009866947,"threshold_uncertainty_score":0.05218208,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148429219","doi":"10.1214/10-aoas378","title":"Detecting multiple authorship of United States Supreme Court legal decisions using function words","year":2011,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Supreme court; Style (visual arts); Function (biology); Legal writing; Writing style; Statistical analysis","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.2448864315332253,"gpt":0.347889792177648,"spread":0.1030033606444227,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00575741,0.0004954014,0.0004931457,0.01025941,0.001046472,0.002671517,0.0004690576,0.0007387932,0.001435474],"category_scores_gemma":[0.08043879,0.0002283329,0.0003710158,0.005525404,0.00118372,0.002737869,0.001476462,0.0007525908,0.0005552983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005420168,"about_ca_system_score_gemma":0.0006207642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008307722,"about_ca_topic_score_gemma":0.001241353,"domain_scores_codex":[0.992353,0.003857824,0.001169582,0.0009074754,0.00130895,0.0004031732],"domain_scores_gemma":[0.8277453,0.1250159,0.02660729,0.009003758,0.009429147,0.002198548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005703649,0.0001551373,0.8283071,0.0001797888,0.0001352689,0.0005070437,0.008539611,0.001753425,0.008934806,0.003688864,0.001065399,0.1461632],"study_design_scores_gemma":[0.00005359523,0.0004381701,0.8638601,0.0001906005,0.0002016015,0.002242994,0.01326714,0.06342433,0.02217809,0.02719643,0.006750641,0.0001963639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905393,0.0001814239,0.007059358,0.00007097789,0.00002763042,0.00002321938,0.000244178,0.00006341421,0.001790432],"genre_scores_gemma":[0.9962519,0.00003719314,0.003156318,0.00001005826,0.00001995221,0.0000147895,0.0002245507,0.00001441085,0.0002708788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01025941,"threshold_uncertainty_score":0.03044844,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3018701121","doi":"10.1214/20-aoas1348","title":"Markov decision processes with dynamic transition probabilities: An analysis of shooting strategies in basketball","year":2020,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Basketball; Transition (genetics); Markov chain; Computer science; Machine learning; History; Chemistry","authors":[{"name":"Nathan Sandholtz","is_ca":true},{"name":"Luke Bornn","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05151780571094042,"gpt":0.2760733188156001,"spread":0.2245555131046597,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002411613,0.000701418,0.0009523337,0.0008608297,0.0005356455,0.001405615,0.001481186,0.001193962,0.004720429],"category_scores_gemma":[0.009307036,0.0006332397,0.000862905,0.00067942,0.001212934,0.001753355,0.00109112,0.001829088,0.0002725462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00160422,"about_ca_system_score_gemma":0.001501395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01746851,"about_ca_topic_score_gemma":0.01174392,"domain_scores_codex":[0.9992622,0.0002832299,0.00002748403,0.000126895,0.0001301207,0.0001700952],"domain_scores_gemma":[0.9951794,0.003592876,0.0005851621,0.0001214836,0.0002297797,0.0002913157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004867548,0.00003858373,0.002301773,0.00002366805,0.00003731524,0.0001048928,0.00009719979,0.9294245,0.0004300952,0.06403445,0.0003509364,0.003107891],"study_design_scores_gemma":[0.00000801619,0.0000182714,0.0004586357,0.000006103132,0.000007386036,0.000011959,0.00003214234,0.9818886,0.00006531749,0.01731151,0.0001845108,0.000007624521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4325787,0.0006084729,0.5558171,0.00120028,0.00006252014,0.0001473383,0.0003812404,0.0001623071,0.009041965],"genre_scores_gemma":[0.9781797,0.0003545569,0.01573916,0.00009358401,0.00002339263,0.00009302937,0.0001565233,0.00003955713,0.005320502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01746851,"threshold_uncertainty_score":0.03473365,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068388041","doi":"10.1214/09-aoas318","title":"Modeling hourly ozone concentration fields","year":2010,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"","keywords":"Bayesian probability; Kriging; Computer science; Kalman filter; Computation; Point process; Algorithm; Machine learning; Mathematics; Statistics; Artificial intelligence","authors":[{"name":"Yiping Dou","is_ca":true},{"name":"Nhu D. Le","is_ca":true},{"name":"James V. Zidek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1730773880426103,"gpt":0.2705241227258004,"spread":0.0974467346831901,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007043211,0.0002506911,0.0003339209,0.0004555814,0.0002499269,0.0005016704,0.0009225458,0.000679598,0.001110339],"category_scores_gemma":[0.001901069,0.0003249922,0.0004251439,0.0005697976,0.0002197279,0.0007775957,0.0005121649,0.0005534284,0.0001303332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000840836,"about_ca_system_score_gemma":0.001147409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04550872,"about_ca_topic_score_gemma":0.04487104,"domain_scores_codex":[0.9997211,0.000112579,0.000009097871,0.00004610601,0.0000787786,0.00003236994],"domain_scores_gemma":[0.9996465,0.0002060921,0.00004364612,0.00003040404,0.00005343143,0.00001999047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002389182,0.00003348648,0.003187458,0.00001716466,0.00002295952,0.00001266464,0.00003316156,0.9675766,0.0005668076,0.01051942,0.0004490125,0.01755734],"study_design_scores_gemma":[0.00000608897,0.000007391384,0.0008883316,0.000002004634,0.000002480708,0.000002398839,0.000005797217,0.9965761,0.0001404836,0.002042406,0.000322967,0.000003620561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2229747,0.0003872878,0.7633114,0.0007944046,0.00005333072,0.00008470894,0.001375365,0.0007803679,0.01023845],"genre_scores_gemma":[0.9046593,0.0003023807,0.09126516,0.00008810721,0.00003937528,0.0001051535,0.0009122515,0.00005254583,0.002575744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04550872,"threshold_uncertainty_score":0.09048766,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070512361","doi":"10.1214/12-aoas566","title":"Dating medieval English charters","year":2012,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Toronto","funders":"","keywords":"Documentation; History; Politics; Genealogy; Phrase; Variation (astronomy); Classics; Law; Computer science; Political science; Natural language processing","authors":[{"name":"Gelila Tilahun","is_ca":true},{"name":"Andrey Feuerverger","is_ca":true},{"name":"Michael Gervers","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05168577421080451,"gpt":0.3030270209621101,"spread":0.2513412467513056,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001679475,0.0002402172,0.0002739971,0.006045381,0.0008487469,0.00138417,0.0004520846,0.0002180657,0.00286223],"category_scores_gemma":[0.01104202,0.0001843891,0.0001302906,0.006311666,0.0007287977,0.0009616214,0.001011209,0.0003686116,0.0008766071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001471604,"about_ca_system_score_gemma":0.001072705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06280338,"about_ca_topic_score_gemma":0.1372446,"domain_scores_codex":[0.9987808,0.0003006943,0.0001763293,0.0002661094,0.0003527028,0.0001233796],"domain_scores_gemma":[0.9906139,0.002307265,0.001821515,0.001113199,0.00388557,0.0002585784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005005631,0.00007865059,0.4945468,0.0008124716,0.00008531215,0.0009023602,0.04182563,0.004229955,0.005337087,0.01885187,0.02940914,0.4034202],"study_design_scores_gemma":[0.00001385465,0.00004998825,0.7807599,0.0002462522,0.00004148134,0.0003278762,0.009463099,0.002722879,0.006851298,0.001374319,0.1980967,0.00005240239],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443814,0.002703474,0.01302501,0.0002508287,0.0001512017,0.0001343058,0.01896066,0.0001751411,0.02021808],"genre_scores_gemma":[0.9550959,0.001900236,0.01729179,0.00002968955,0.00007868993,0.0001183056,0.01444343,0.00009410956,0.01094791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06280338,"threshold_uncertainty_score":0.1248757,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2890389585","doi":"10.1214/17-aoas1130","title":"Estimating and comparing cancer progression risks under varying surveillance protocols","year":2018,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Cancer Institute; National Institutes of Health; Prostate Cancer Canada; Genomic Health; Canary Foundation; U.S. Department of Defense","keywords":"Prostate cancer; Schema (genetic algorithms); Medicine; Cancer; Computer science; Econometrics; Machine learning; Internal medicine; Mathematics","authors":[{"name":"Jane Lange","is_ca":true},{"name":"Janet E. Cowan","is_ca":true},{"name":"Lawrence Klotz","is_ca":true},{"name":"Ruth Etzioni","is_ca":true},{"name":"Roman Gulati","is_ca":true},{"name":"Amy Leonardson","is_ca":true},{"name":"Daniel W. Lin","is_ca":true},{"name":"Lisa F. Newcomb","is_ca":true},{"name":"Bruce J. Trock","is_ca":true},{"name":"H. Ballentine Carter","is_ca":true},{"name":"Peter R. Carroll","is_ca":true},{"name":"Matthew R. Cooperberg","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4732441895858425,"gpt":0.5406745580878244,"spread":0.06743036850198192,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05986971,0.0006917927,0.001154105,0.001585732,0.0004620252,0.001900726,0.001775264,0.001777796,0.0006334326],"category_scores_gemma":[0.1837917,0.0006673409,0.001576327,0.001365474,0.001585416,0.002206224,0.001759156,0.001836671,0.00009038324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001659335,"about_ca_system_score_gemma":0.002253203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005874543,"about_ca_topic_score_gemma":0.003346405,"domain_scores_codex":[0.9815231,0.01413817,0.000749018,0.002340848,0.000860002,0.0003889239],"domain_scores_gemma":[0.7810054,0.1992151,0.01031666,0.007243047,0.001660873,0.000558889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001439496,0.0004767892,0.1480073,0.0004293533,0.00201636,0.0002564935,0.000592467,0.7186384,0.001793344,0.03224095,0.0006983701,0.09341065],"study_design_scores_gemma":[0.0002943845,0.001096847,0.04962424,0.0001469624,0.0009042099,0.0001731673,0.0002890056,0.8691006,0.002818115,0.07436682,0.001088257,0.0000973441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7450981,0.001277929,0.2504741,0.0009348978,0.00004902669,0.000298998,0.0006864023,0.0001366371,0.001044052],"genre_scores_gemma":[0.927125,0.0007154355,0.07018586,0.000146305,0.00006418105,0.0003995911,0.0009182455,0.00002791837,0.0004173429],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05986971,"threshold_uncertainty_score":0.3166251,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2789596487","doi":"10.1214/17-aoas1108","title":"Simultaneous modelling of movement, measurement error, and observer dependence in mark-recapture distance sampling: An application to Arctic bird surveys","year":2018,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Estimator; Distance sampling; Mark and recapture; Observer (physics); Statistics; Abundance estimation; Sampling (signal processing); Observational error; Computer science; Abundance (ecology); Mathematics; Econometrics; Ecology; Biology; Computer vision","authors":[{"name":"Paul B. Conn","is_ca":true},{"name":"Ray T. Alisauskas","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1082806134048536,"gpt":0.3023662759143145,"spread":0.1940856625094609,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008121854,0.0009433949,0.00112253,0.0007116582,0.0006858155,0.00112831,0.002501464,0.001619334,0.001126841],"category_scores_gemma":[0.02519613,0.00112815,0.001748169,0.001077484,0.001301016,0.00140847,0.001838159,0.001731425,0.0002028049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001598923,"about_ca_system_score_gemma":0.001873435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06587785,"about_ca_topic_score_gemma":0.04660486,"domain_scores_codex":[0.9970269,0.001783687,0.0001187506,0.0005228143,0.0003251244,0.0002229005],"domain_scores_gemma":[0.9814555,0.01457689,0.002165729,0.000763381,0.0007737987,0.0002646318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001069653,0.00006572064,0.03721638,0.0001186475,0.0002234801,0.0004371078,0.0005221014,0.8967422,0.001526646,0.0299078,0.0005322289,0.03260076],"study_design_scores_gemma":[0.000008308454,0.00003713236,0.004977864,0.00000996966,0.00003653848,0.00008204492,0.00002736037,0.9880194,0.0002008703,0.005851951,0.0007249608,0.00002362292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06782605,0.0003307511,0.9305486,0.0002931013,0.00002922878,0.00005290659,0.0001133843,0.0001335516,0.0006723753],"genre_scores_gemma":[0.8237017,0.000632785,0.1700836,0.0001252967,0.0001213707,0.0002159336,0.0003439669,0.0001147569,0.004660578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06587785,"threshold_uncertainty_score":0.1309888,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2884750441","doi":"10.1214/18-aoas1160sf","title":"When should modes of inference disagree? Some simple but challenging examples","year":2018,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":12,"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":"Frequentist inference; Fiducial inference; Inference; Bayes' theorem; Computer science; Simple (philosophy); Bayesian inference; Statistical inference; Artificial intelligence; Machine learning; Predictive inference; Econometrics; Bayesian probability; Mathematics; Epistemology; Statistics; Philosophy","authors":[{"name":"D. A. S. Fraser","is_ca":true},{"name":"Nancy Reid","is_ca":true},{"name":"Wei Lin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2025395505903315,"gpt":0.3616324196098393,"spread":0.1590928690195079,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04107627,0.001295104,0.00222852,0.003164802,0.008244418,0.006687458,0.004150317,0.01085994,0.005212804],"category_scores_gemma":[0.2016889,0.001050407,0.001578622,0.005045661,0.01537316,0.01540862,0.004873789,0.009961688,0.000954547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00252133,"about_ca_system_score_gemma":0.001294242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002526675,"about_ca_topic_score_gemma":0.001995458,"domain_scores_codex":[0.967478,0.02330098,0.0013465,0.002270826,0.004569087,0.001034527],"domain_scores_gemma":[0.8215755,0.1601243,0.003802569,0.005852664,0.007502695,0.001142219],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001634204,0.00003722926,0.001461973,0.0002130646,0.00007233881,0.0009066187,0.002872131,0.004267856,0.0001312387,0.9500809,0.01494855,0.02484462],"study_design_scores_gemma":[0.00002540206,0.000006518391,0.0002778601,0.00008295096,0.000007040322,0.0001806031,0.0004199092,0.004903016,0.00005554282,0.9883994,0.005621218,0.00002054616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04832118,0.01372127,0.6191606,0.1969403,0.001435665,0.0002975413,0.0005840005,0.000554028,0.1189855],"genre_scores_gemma":[0.7482536,0.006176961,0.2192751,0.01431379,0.002697793,0.0006533566,0.0004149919,0.0003989504,0.00781529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9589238,"threshold_uncertainty_score":0.2172346,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2042759248","doi":"10.1214/14-aoas804","title":"A two-step approach to model precipitation extremes in California based on max-stable and marginal point processes","year":2015,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Maxima; Extreme value theory; Pairwise comparison; Statistics; Univariate; Mathematics; Block (permutation group theory); Maxima and minima; Generalized extreme value distribution; Confidence interval; Point estimation; Precipitation; Econometrics; Multivariate statistics; Meteorology; Geography; Combinatorics; Mathematical analysis","authors":[{"name":"Hongwei Shang","is_ca":false},{"name":"Jun Yan","is_ca":false},{"name":"Xuebin Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05744828277066105,"gpt":0.2899532334536131,"spread":0.232504950682952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00149913,0.0004597072,0.0004619283,0.0005351242,0.0004849935,0.0006728861,0.001532399,0.0006444431,0.001818295],"category_scores_gemma":[0.00254132,0.0004158199,0.001035137,0.0004822747,0.000587634,0.0009084611,0.001075949,0.001114257,0.0001540517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006324719,"about_ca_system_score_gemma":0.0009281014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008346222,"about_ca_topic_score_gemma":0.01165483,"domain_scores_codex":[0.9995958,0.0001980623,0.00001738515,0.00008332911,0.00006777891,0.00003756715],"domain_scores_gemma":[0.999358,0.0004150386,0.0000636482,0.00004261007,0.00007644016,0.00004425878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008339152,0.00004552358,0.004247198,0.00002350354,0.00006108692,0.00009042004,0.0001133989,0.9470827,0.0008632771,0.03459671,0.0003787141,0.01241398],"study_design_scores_gemma":[0.000005837332,0.0000199242,0.0004393281,0.000001768147,0.000008505677,0.00001098879,0.000005989453,0.9921048,0.00008472157,0.007105645,0.0002057145,0.000006852249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08942405,0.0001395917,0.907497,0.0001969546,0.00001965051,0.00005705587,0.0001448019,0.0001701738,0.002350622],"genre_scores_gemma":[0.8494677,0.0001687082,0.1453061,0.00006326978,0.00003328847,0.0002833617,0.0002501524,0.00005348641,0.004374061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008346222,"threshold_uncertainty_score":0.0165953,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3037512058","doi":"10.1214/20-aoas1331","title":"Focused model selection for linear mixed models with an application to whale ecology","year":2020,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Norges Forskningsråd","keywords":"Whaling; Generalized linear mixed model; Model selection; Mixed model; Estimator; Linear model; Selection (genetic algorithm); Computer science; Whale; Information Criteria; Ecology; Econometrics; Mathematics; Statistics; Artificial intelligence; Machine learning; Biology","authors":[{"name":"Céline Cunen","is_ca":true},{"name":"Lars Walløe","is_ca":false},{"name":"Nils Lid Hjort","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1948717162346481,"gpt":0.4055610505485011,"spread":0.210689334313853,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03598071,0.001890138,0.002335675,0.00401691,0.001116334,0.002375488,0.003364263,0.002527558,0.00409041],"category_scores_gemma":[0.1045755,0.001193182,0.003439154,0.003116605,0.003974247,0.003305654,0.004337243,0.004864979,0.0008323411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001964794,"about_ca_system_score_gemma":0.002231625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002949455,"about_ca_topic_score_gemma":0.002993831,"domain_scores_codex":[0.975385,0.02103066,0.0006109961,0.001188919,0.00151724,0.0002671653],"domain_scores_gemma":[0.8885608,0.1033599,0.002343843,0.002662751,0.002660828,0.0004119197],"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":[0.00007714122,0.0000626704,0.001571837,0.0006238105,0.0004832358,0.0003498229,0.0005830312,0.09062429,0.001027536,0.824905,0.003813326,0.07587843],"study_design_scores_gemma":[0.00002414368,0.00008611524,0.0003880698,0.0001420859,0.00006929546,0.0001212413,0.00005280622,0.3555411,0.0005164742,0.6370133,0.006000423,0.00004492419],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006595029,0.0004271358,0.9981927,0.0002714041,0.00002655973,0.00002590506,0.00003330364,0.00006351927,0.0002999003],"genre_scores_gemma":[0.05881895,0.001814671,0.9351552,0.0006417313,0.0005088179,0.0008501384,0.0004111987,0.000309546,0.00148976],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03598071,"threshold_uncertainty_score":0.1902865,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2605027696","doi":"10.1214/16-aoas1002","title":"Electricity price dependence in New York State zones: A robust detrended correlation approach","year":2017,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Electric Power System Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Econometrics; Electricity; Detrended fluctuation analysis; Grid; Estimator; Electricity price; Computer science; Economics; Statistics; Mathematics","authors":[{"name":"Debbie J. Dupuis","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0574632283859776,"gpt":0.2638624869674593,"spread":0.2063992585814817,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002686613,0.0004489879,0.0006260707,0.001055449,0.0003373578,0.001183732,0.001340329,0.0006855129,0.001325507],"category_scores_gemma":[0.01372883,0.0004709887,0.0007519173,0.0009972702,0.0007583509,0.001268825,0.0009776908,0.001409296,0.000169325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007762989,"about_ca_system_score_gemma":0.0006117352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02837881,"about_ca_topic_score_gemma":0.02179072,"domain_scores_codex":[0.9991804,0.0004294837,0.00004112846,0.0001820837,0.0001016395,0.00006537404],"domain_scores_gemma":[0.99176,0.005864304,0.0009177586,0.0006880751,0.0005955024,0.0001743488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002582575,0.0001677529,0.08888382,0.00009161066,0.0005407264,0.0007252579,0.0002224218,0.8296993,0.001291934,0.04051924,0.00280985,0.0347899],"study_design_scores_gemma":[0.000003576597,0.000007711304,0.005929563,0.000005639494,0.00001076026,0.00001480551,0.00001838514,0.9903283,0.0001110385,0.003283393,0.000276826,0.0000100047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6923956,0.0008523366,0.3011491,0.0009730898,0.00008285465,0.00007083888,0.001105813,0.0002467749,0.00312357],"genre_scores_gemma":[0.9830638,0.0002493898,0.01406789,0.0000518472,0.00005482667,0.00004353707,0.001030304,0.00004228805,0.001396064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02837881,"threshold_uncertainty_score":0.05642724,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012397316","doi":"10.1214/14-aoas774","title":"Evaluating epoetin dosing strategies using observational longitudinal data","year":2014,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":9,"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":"Medicine; Observational study; Dosing; Regimen; Kidney disease; Intensive care medicine; Proportional hazards model; Dialysis; Anemia; Marginal structural model; Randomized controlled trial; Hemodialysis; Epoetin alfa; Clinical trial; Survival analysis; Internal medicine","authors":[{"name":"Cecilia A. Cotton","is_ca":true},{"name":"Patrick J. Heagerty","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.9581370777790906,"gpt":0.6916552922079258,"spread":0.2664817855711649,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12217,0.001022558,0.001402044,0.001370682,0.0005965897,0.001451624,0.001950492,0.002007581,0.00142487],"category_scores_gemma":[0.2539806,0.0006246307,0.002771084,0.001528064,0.001342264,0.001915041,0.001771542,0.002006738,0.000231212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001663485,"about_ca_system_score_gemma":0.002453509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005667508,"about_ca_topic_score_gemma":0.004548207,"domain_scores_codex":[0.933827,0.05932428,0.00190451,0.002605418,0.001686852,0.0006520061],"domain_scores_gemma":[0.5850837,0.3664041,0.02612159,0.01665314,0.004075227,0.001662293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01244598,0.002935095,0.4738051,0.0007858087,0.00904821,0.0003717334,0.0009372373,0.3588532,0.001025031,0.01490651,0.00217621,0.1227099],"study_design_scores_gemma":[0.001816559,0.01074205,0.07082165,0.0002029868,0.002395799,0.0001592127,0.000411355,0.8878393,0.001517399,0.02193379,0.002027202,0.0001327233],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8860937,0.001471413,0.1074834,0.00136148,0.000115743,0.0007818246,0.001617513,0.0001785185,0.0008963159],"genre_scores_gemma":[0.9690387,0.0004086759,0.02817684,0.0002243175,0.0000657598,0.0006416365,0.00112064,0.00001790645,0.0003055941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.12217,"threshold_uncertainty_score":0.6461046,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2492804687","doi":"10.1214/16-aoas909","title":"A Bayesian graphical model for genome-wide association studies (GWAS)","year":2016,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; York University; University of Toronto","funders":"National Cancer Institute","keywords":"Genome-wide association study; Computer science; Bayesian probability; Genetic association; Graphical model; Single-nucleotide polymorphism; Computational biology; Data mining; Machine learning; Artificial intelligence; Biology; Genetics","authors":[{"name":"Laurent Briollais","is_ca":true},{"name":"Adrian Dobra","is_ca":true},{"name":"Jinnan Liu","is_ca":true},{"name":"Matt Friedlander","is_ca":true},{"name":"Hilmi Özçelik","is_ca":true},{"name":"Hélène Massam","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0591072333190735,"gpt":0.3411212849881219,"spread":0.2820140516690484,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00845036,0.00132209,0.002114203,0.002873231,0.0008377918,0.002563148,0.004028929,0.003268032,0.006593019],"category_scores_gemma":[0.02655727,0.001228271,0.002403185,0.003913591,0.002462807,0.002924358,0.001896511,0.00374599,0.002414988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001740484,"about_ca_system_score_gemma":0.002204609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0116859,"about_ca_topic_score_gemma":0.01159212,"domain_scores_codex":[0.9952678,0.003145665,0.0002051311,0.0006664172,0.0005536454,0.0001613259],"domain_scores_gemma":[0.9878085,0.01024623,0.0006829773,0.0005383276,0.0005289399,0.0001950351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001459676,0.00005409744,0.002677333,0.0003433051,0.0002184654,0.0002746547,0.0002233976,0.4591268,0.001017094,0.4713912,0.008050198,0.0564773],"study_design_scores_gemma":[0.00005575615,0.00003338503,0.0004984529,0.00005006584,0.00005223649,0.0001416217,0.00002037546,0.5703768,0.000118617,0.422601,0.006012856,0.0000387586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001460339,0.0003964225,0.9960746,0.0004922791,0.0000368297,0.00003650157,0.0005482687,0.0003085305,0.0006462994],"genre_scores_gemma":[0.1741116,0.003118771,0.81037,0.001206046,0.0004748741,0.001262889,0.004008169,0.0003957113,0.005051814],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0116859,"threshold_uncertainty_score":0.04469031,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2028703934","doi":"10.1214/14-aoas747","title":"Imputation of truncated p-values for meta-analysis methods and its genomic application","year":2014,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; University of Toronto","funders":"National Institute of Mental Health","keywords":"Inference; Imputation (statistics); False discovery rate; Raw data; Genomics; Statistical power; Multiple comparisons problem","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.106927202962884,"gpt":0.4195952967598843,"spread":0.3126680937970003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09926233,0.001210945,0.002781547,0.002051856,0.0006154513,0.003462491,0.003574252,0.002294715,0.003367133],"category_scores_gemma":[0.3050117,0.001015109,0.00433594,0.004504976,0.001287158,0.002417851,0.002159508,0.00441591,0.0008586056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255586,"about_ca_system_score_gemma":0.002586258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308527,"about_ca_topic_score_gemma":0.001411985,"domain_scores_codex":[0.8949673,0.09291331,0.003319055,0.003828209,0.004570897,0.0004012475],"domain_scores_gemma":[0.7368174,0.2317269,0.006672794,0.01853096,0.005822635,0.0004293321],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001068094,0.0001320457,0.02209146,0.005302738,0.01542736,0.001153489,0.0005929637,0.1986666,0.002264348,0.1589216,0.01566453,0.5787148],"study_design_scores_gemma":[0.0004939796,0.0005968296,0.007731185,0.001466893,0.003677885,0.001442763,0.0001048608,0.5647365,0.003832545,0.382968,0.03275042,0.000198009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001822385,0.003725058,0.9925212,0.0005642364,0.0001515151,0.000123577,0.0003431188,0.0003570046,0.000391873],"genre_scores_gemma":[0.1072465,0.002769875,0.8864388,0.000678802,0.0002585421,0.0009650266,0.0007832517,0.0002136163,0.0006454741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9007376,"threshold_uncertainty_score":0.5249556,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2043089632","doi":"10.1214/12-aoas537","title":"A model for sequential evolution of ligands by exponential enrichment (SELEX) data","year":2012,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Systematic evolution of ligands by exponential enrichment; Oligonucleotide; Computational biology; Biology; DNA; Computer science; Genetics; RNA","authors":[{"name":"Juli Atherton","is_ca":true},{"name":"Nathan Boley","is_ca":true},{"name":"B. H. Brown","is_ca":true},{"name":"Nobuo Ogawa","is_ca":true},{"name":"Stuart M. Davidson","is_ca":true},{"name":"Michael B. Eisen","is_ca":true},{"name":"Mark D. Biggin","is_ca":true},{"name":"Peter J. Bickel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08406956993041677,"gpt":0.3643286796780921,"spread":0.2802591097476754,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004474633,0.001830582,0.002777398,0.001362257,0.0008011494,0.002525362,0.004293473,0.003929024,0.007541339],"category_scores_gemma":[0.0159659,0.001250909,0.001880114,0.001399848,0.002350837,0.003027982,0.001956016,0.003338608,0.001841924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00213651,"about_ca_system_score_gemma":0.001852161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01048728,"about_ca_topic_score_gemma":0.005101532,"domain_scores_codex":[0.9979118,0.0006902599,0.0001238589,0.0006535137,0.0003025339,0.0003181237],"domain_scores_gemma":[0.9904061,0.006922055,0.0009178024,0.0005066629,0.0008039379,0.0004434255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000207742,0.00007780288,0.002298427,0.0001181804,0.00006673804,0.0002497535,0.0001677844,0.942601,0.002339263,0.04512472,0.001537879,0.005210665],"study_design_scores_gemma":[0.0000459711,0.00003847966,0.0003292924,0.00001096305,0.00001659978,0.00004341608,0.00001422049,0.9864775,0.0003555435,0.01189479,0.0007513403,0.00002182797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1449755,0.0007826888,0.8371168,0.003591996,0.0001580452,0.0004553614,0.003674537,0.001327958,0.007917084],"genre_scores_gemma":[0.8410969,0.001291884,0.1068058,0.001322504,0.0001531946,0.002537117,0.004482165,0.0004202429,0.04189031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01048728,"threshold_uncertainty_score":0.02522826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297333613","doi":"10.1214/22-aoas1600","title":"Bayesian hierarchical random-effects meta-analysis and design of phase I clinical trials","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"National Cancer Institute","keywords":"Meta-analysis; Random effects model; Computer science; Bayesian probability; Parametric statistics; Clinical study design; Statistics; Clinical trial; Data mining; Artificial intelligence; Mathematics; Medicine","authors":[{"name":"Ruitao Lin","is_ca":false},{"name":"Haolun Shi","is_ca":true},{"name":"Guosheng Yin","is_ca":false},{"name":"Peter F. Thall","is_ca":false},{"name":"Ying Yuan","is_ca":false},{"name":"Christopher R. Flowers","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.8487982915840147,"gpt":0.6507765650971692,"spread":0.1980217264868455,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.131666,0.003125574,0.007433944,0.007775874,0.0009088187,0.004170042,0.006577189,0.003182594,0.003062047],"category_scores_gemma":[0.217425,0.00297037,0.01106321,0.005801319,0.002007113,0.003665709,0.002998836,0.004657174,0.0006022953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003542911,"about_ca_system_score_gemma":0.006375748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003312882,"about_ca_topic_score_gemma":0.00387966,"domain_scores_codex":[0.7909615,0.192903,0.004296406,0.005802768,0.005367464,0.0006688659],"domain_scores_gemma":[0.8664356,0.1114859,0.007796232,0.01002133,0.003536657,0.0007242665],"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":[0.003602895,0.0003134651,0.005389696,0.01407881,0.04901961,0.0003541985,0.0004844114,0.481815,0.001315927,0.1485321,0.00870699,0.2863869],"study_design_scores_gemma":[0.004015224,0.001178563,0.002623238,0.001694545,0.02062061,0.000233832,0.00005205981,0.592342,0.001681501,0.3646747,0.01057096,0.0003127771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001736749,0.006805616,0.9881512,0.0006869654,0.0001949419,0.001103207,0.0003384758,0.0004465555,0.0005363466],"genre_scores_gemma":[0.1158566,0.004770738,0.870261,0.0009578235,0.0003414775,0.006332862,0.0007050175,0.0001800844,0.0005943094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.131666,"threshold_uncertainty_score":0.6963248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320169867","doi":"10.1214/22-aoas1639","title":"Modeling panels of extremes","year":2023,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation HEC; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Pooling; Extreme value theory; Inference; Computer science; Regression; Econometrics; Statistics; Regression analysis; Data mining; Mathematics; Machine learning; Artificial intelligence","authors":[{"name":"Debbie J. Dupuis","is_ca":true},{"name":"Sebastian Engelke","is_ca":false},{"name":"Luca Trapin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2390188881059419,"gpt":0.3158408307269341,"spread":0.07682194262099223,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003242468,0.0005902104,0.00112914,0.0009728071,0.0006256769,0.001755442,0.001911147,0.00176795,0.004874606],"category_scores_gemma":[0.01338823,0.0006159064,0.001361451,0.001481478,0.0009412115,0.001923713,0.001987476,0.002306839,0.0009269296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006499506,"about_ca_system_score_gemma":0.0005024349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004407534,"about_ca_topic_score_gemma":0.00375404,"domain_scores_codex":[0.9980727,0.0009990291,0.00004953792,0.0005167287,0.0001881732,0.000173745],"domain_scores_gemma":[0.9944805,0.003484528,0.0006778569,0.0008735896,0.0002911706,0.0001924054],"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":[0.0001116211,0.00008729324,0.01832706,0.00008339244,0.0002099525,0.0002571521,0.0003281436,0.8099087,0.001915295,0.1267615,0.004167259,0.0378427],"study_design_scores_gemma":[0.00001418966,0.00002378348,0.002943473,0.00001605971,0.00002183621,0.00004069617,0.00003786794,0.9046796,0.0002700464,0.08965204,0.002281695,0.00001877277],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06162567,0.0002629044,0.9328638,0.0003934717,0.00006266153,0.00007186845,0.001021729,0.0004065991,0.003291258],"genre_scores_gemma":[0.8213528,0.0005621497,0.1671354,0.0004193989,0.0002099108,0.0004685498,0.003363904,0.0002032745,0.006284636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004874606,"threshold_uncertainty_score":0.01714796,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1965857117","doi":"10.1214/08-aoas199","title":"$\\mathcal{G}$-SELC: Optimization by sequential elimination of level combinations using genetic algorithms and Gaussian processes","year":2009,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Computational Drug Discovery Methods","field":"Computer Science","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":"University of Georgia Research Foundation; Acadia University; University of Georgia; Pfizer; National Science Foundation","keywords":"Gaussian process; Set (abstract data type); Process (computing); Gaussian; Data set; Genetic algorithm","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.107672180175107,"gpt":0.3684116037186514,"spread":0.2607394235435444,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002720332,0.001325334,0.002156973,0.001981666,0.0006288673,0.0009227822,0.002033358,0.001887781,0.00236867],"category_scores_gemma":[0.005050357,0.0007122087,0.001570637,0.002470378,0.001469283,0.00101027,0.001663824,0.001997285,0.0006906807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001080147,"about_ca_system_score_gemma":0.002453751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007407714,"about_ca_topic_score_gemma":0.008019933,"domain_scores_codex":[0.9991358,0.0004196653,0.00003917592,0.0001082759,0.0002185258,0.00007849234],"domain_scores_gemma":[0.9975945,0.001812603,0.0001421232,0.0001409147,0.0002458473,0.00006399227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001617641,0.000167443,0.001518424,0.0001159762,0.0001657436,0.00009665997,0.00008066606,0.7611244,0.001453522,0.02019557,0.00342737,0.2114924],"study_design_scores_gemma":[0.00002701276,0.00003429141,0.00008190379,0.000004690611,0.00001309748,0.00001447324,0.000004889698,0.9938822,0.0004443631,0.005043632,0.0004442779,0.000005229791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01052716,0.000187019,0.987206,0.0002118902,0.00002347157,0.00007633025,0.00005631466,0.0006675072,0.001044293],"genre_scores_gemma":[0.1343759,0.0001920075,0.8626105,0.0003261977,0.00003799087,0.000357239,0.0002673737,0.0002354244,0.00159733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007407714,"threshold_uncertainty_score":0.0147292,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388054702","doi":"10.1214/23-aoas1776","title":"Continuous-time modelling of behavioural responses in animal movement","year":2023,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Bureau of Medicine and Surgery; National Marine Fisheries Service; U.S. Department of Defense; U.S. Fleet Forces Command; Office of Naval Research; U.S. Navy","keywords":"Baseline (sea); Nonparametric statistics; Computer science; Covariate; Stochastic modelling; Sound exposure; Statistics; Machine learning; Mathematics","authors":[{"name":"Théo Michelot","is_ca":true},{"name":"Richard Glennie","is_ca":false},{"name":"Len Thomas","is_ca":false},{"name":"Nicola J. Quick","is_ca":false},{"name":"Catriona M. Harris","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1385718350172995,"gpt":0.3100216156854771,"spread":0.1714497806681776,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002322017,0.0007768309,0.0007468545,0.0006846233,0.0002936244,0.001208927,0.00239179,0.001614456,0.002442226],"category_scores_gemma":[0.008423836,0.0005166671,0.001370281,0.0008361923,0.001147627,0.0009334729,0.0009411649,0.00163717,0.0003494266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009739083,"about_ca_system_score_gemma":0.000800423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02419041,"about_ca_topic_score_gemma":0.01081415,"domain_scores_codex":[0.999151,0.0003220159,0.00005397535,0.0002450308,0.0001273284,0.0001006935],"domain_scores_gemma":[0.9954389,0.003264394,0.0005565173,0.000225936,0.0003488072,0.0001654739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003652053,0.00004286222,0.003500331,0.00005809875,0.00005723523,0.000078607,0.0001229688,0.9763509,0.0007627254,0.01529792,0.0002157754,0.003476119],"study_design_scores_gemma":[0.000002870146,0.000007505645,0.0005088284,0.000002570085,0.000004407567,0.000004613064,0.000006648922,0.9971802,0.00003208441,0.002111746,0.0001344891,0.000003964063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1669488,0.0005554424,0.8269525,0.0007288929,0.0001389277,0.00008514143,0.0009397817,0.0004443916,0.003206199],"genre_scores_gemma":[0.9576732,0.0004260046,0.03301469,0.0001080747,0.00005647992,0.0002731431,0.0008047778,0.00006973514,0.007573798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02419041,"threshold_uncertainty_score":0.04809922,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4220666549","doi":"10.1214/21-aoas1499","title":"Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Institute of Genetics; Canadian Institutes of Health Research; European Commission","keywords":"Coronavirus disease 2019 (COVID-19); Bayesian probability; Pandemic; Statistics; Case fatality rate; Econometrics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Identifiability; Demography; 2019-20 coronavirus outbreak; Population; Statistical hypothesis testing; Mathematics; Geography; Actuarial science; Medicine; Economics; Virology; Sociology; Outbreak","authors":[{"name":"Harlan Campbell","is_ca":true},{"name":"Perry de Valpine","is_ca":false},{"name":"Lauren Maxwell","is_ca":false},{"name":"Valentijn M. T. de Jong","is_ca":false},{"name":"Thomas P. A. Debray","is_ca":false},{"name":"Thomas Jaenisch","is_ca":false},{"name":"Paul Gustafson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5179263313108267,"gpt":0.4970631588051286,"spread":0.02086317250569808,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1348215,0.001253868,0.00246689,0.001948703,0.001373186,0.002841642,0.004420705,0.002999809,0.001960096],"category_scores_gemma":[0.3909401,0.001480212,0.002409198,0.002765444,0.005560198,0.004015801,0.003641571,0.005745742,0.0003064639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002331473,"about_ca_system_score_gemma":0.003190097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01287949,"about_ca_topic_score_gemma":0.01102256,"domain_scores_codex":[0.8821077,0.1053736,0.002272789,0.005405396,0.003685819,0.00115462],"domain_scores_gemma":[0.6324457,0.3276676,0.01509724,0.017827,0.005809401,0.001153169],"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":[0.001400547,0.000190391,0.08502714,0.000711577,0.00243038,0.001119086,0.001906028,0.3857392,0.002102326,0.3593467,0.006629892,0.1533968],"study_design_scores_gemma":[0.0001454095,0.0003756516,0.01688315,0.0002882406,0.0003213401,0.0006612658,0.0002143289,0.6568577,0.001371063,0.3185419,0.004200266,0.0001395315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04845202,0.001067068,0.9445403,0.002845156,0.0001676694,0.0002505838,0.000223705,0.0002533182,0.002200229],"genre_scores_gemma":[0.628529,0.0007847435,0.3653288,0.001587707,0.0002806826,0.0006427469,0.0006237563,0.0001367163,0.002085906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1348215,"threshold_uncertainty_score":0.7130127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3174385715","doi":"10.1214/21-aoas1570","title":"Estimation of the marginal effect of antidepressants on body mass index under confounding and endogenous covariate-driven monitoring times","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Covariate; Endogeneity; Confounding; Statistics; Body mass index; Propensity score matching; Marginal structural model; Econometrics; Selection bias; Index (typography); Medicine; Computer science; Mathematics; Internal medicine","authors":[{"name":"Janie Coulombe","is_ca":true},{"name":"Erica E. M. Moodie","is_ca":true},{"name":"Robert W. Platt","is_ca":true},{"name":"Christel Renoux","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1758420278101468,"gpt":0.4153412653801332,"spread":0.2394992375699864,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02953714,0.001082416,0.00203444,0.001446484,0.0004529637,0.001644828,0.002583616,0.001810678,0.003688456],"category_scores_gemma":[0.1235551,0.0007503672,0.003412842,0.002523724,0.001893277,0.002615041,0.001939659,0.002444329,0.0003743219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001329532,"about_ca_system_score_gemma":0.002686122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007760918,"about_ca_topic_score_gemma":0.004568446,"domain_scores_codex":[0.9867336,0.009455225,0.0006418884,0.001938634,0.0008436369,0.0003869932],"domain_scores_gemma":[0.8961616,0.09132943,0.005397576,0.005286582,0.001473537,0.0003512397],"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":[0.001132437,0.0003571883,0.1599738,0.002184641,0.005531963,0.001007261,0.001544565,0.2034011,0.001757789,0.3510691,0.003483917,0.2685563],"study_design_scores_gemma":[0.0002779999,0.0006854186,0.05284316,0.0005970642,0.002503081,0.0005724834,0.0002853591,0.5066868,0.002219644,0.4227354,0.01047212,0.0001214917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08193906,0.003007534,0.9105866,0.001364997,0.0001696834,0.0002891085,0.0009431789,0.000200791,0.001499069],"genre_scores_gemma":[0.6937077,0.004872694,0.2925583,0.0008060886,0.0004710756,0.001111279,0.00176031,0.0001157765,0.004596738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02953714,"threshold_uncertainty_score":0.1562092,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1987751717","doi":"10.1214/10-aoas342","title":"A nonlinear mixed effects directional model for the estimation of the rotation axes of the human ankle","year":2010,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rotation (mathematics); Orientation (vector space); Nonlinear system; Measure (data warehouse); Monte Carlo method; Linear model; Sample (material); Data set","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.07298192094243762,"gpt":0.3524230450008768,"spread":0.2794411240584392,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01093781,0.001765454,0.001704644,0.001529189,0.0005720666,0.001408978,0.003702149,0.001785017,0.00436463],"category_scores_gemma":[0.01876997,0.0009219047,0.003405952,0.001590076,0.001344449,0.001680474,0.001561892,0.002325316,0.001789134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151663,"about_ca_system_score_gemma":0.001461312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005125314,"about_ca_topic_score_gemma":0.006318588,"domain_scores_codex":[0.9935592,0.003934981,0.0002412073,0.001330676,0.000697147,0.0002368061],"domain_scores_gemma":[0.9940178,0.004098355,0.0005102934,0.0007372544,0.0005489647,0.00008734893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000818616,0.0002418226,0.008569648,0.0005404757,0.0008718586,0.000334686,0.0006876464,0.4989587,0.005581202,0.3185561,0.003330317,0.161509],"study_design_scores_gemma":[0.0000941101,0.0004587984,0.002582177,0.00006006933,0.0001809641,0.0001885127,0.00005259174,0.8982132,0.001085101,0.09002818,0.006953202,0.0001031119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003596282,0.0001410688,0.9953252,0.00009462325,0.00004524945,0.00009104777,0.0002342207,0.00009164153,0.0003806074],"genre_scores_gemma":[0.1731869,0.0008730247,0.8130144,0.0002817723,0.0002118031,0.002837733,0.001728433,0.0001494276,0.007716542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01093781,"threshold_uncertainty_score":0.05784535,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2569003638","doi":"10.1214/16-aoas964","title":"Inferring rooted population trees using asymmetric neighbor joining","year":2016,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"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":"Population; Artificial intelligence; Computer science; Mathematics; Geography; Demography; Sociology","authors":[{"name":"Yongliang Zhai","is_ca":true},{"name":"Alexandre Bouchard‐Côté","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06098441619541242,"gpt":0.3102171999441594,"spread":0.2492327837487469,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003436654,0.000615696,0.001008757,0.002541529,0.000922864,0.001105063,0.002581632,0.0009772559,0.001063446],"category_scores_gemma":[0.01881455,0.0005900748,0.0008051746,0.002115375,0.0007911288,0.001742077,0.001580339,0.002065336,0.0004809645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005427894,"about_ca_system_score_gemma":0.0007439821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001744265,"about_ca_topic_score_gemma":0.002073514,"domain_scores_codex":[0.99768,0.00107765,0.0001217055,0.0004901133,0.0005265349,0.000104105],"domain_scores_gemma":[0.9910395,0.005714669,0.001227779,0.001202827,0.0006030822,0.0002121964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002199429,0.0001636818,0.02112574,0.0001960572,0.0002923781,0.0003866803,0.0008033587,0.6522746,0.009544293,0.1078644,0.002020691,0.2051083],"study_design_scores_gemma":[0.0000152897,0.00001655694,0.0006950986,0.00001062809,0.00001466147,0.00007214848,0.00003163494,0.9392643,0.001260624,0.05732427,0.001279635,0.00001511132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02680249,0.00009626319,0.9722717,0.00006277881,0.00001560693,0.00002143578,0.0001518036,0.0001710522,0.0004068135],"genre_scores_gemma":[0.3629756,0.0001940862,0.6346349,0.00008513987,0.00006789496,0.0001117394,0.001250564,0.0001450189,0.0005351377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003436654,"threshold_uncertainty_score":0.01817501,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4221109862","doi":"10.1214/21-aoas1518","title":"Accounting for drop-out using inverse probability censoring weights in longitudinal clustered data with informative cluster size","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"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":"Censoring (clinical trials); Statistics; Inverse probability; Inference; Generalized estimating equation; Mathematics; Random effects model; Estimating equations; Marginal structural model; Econometrics; Causal inference; Computer science; Medicine; Estimator; Artificial intelligence; Meta-analysis","authors":[{"name":"Aya Mitani","is_ca":true},{"name":"Kerrie P. Nelson","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1611153341132059,"gpt":0.3564482760153331,"spread":0.1953329419021272,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03812851,0.0009916291,0.001727818,0.002226308,0.0009447747,0.001590964,0.003099265,0.002340362,0.001301967],"category_scores_gemma":[0.1078966,0.0008018413,0.002032375,0.002491419,0.001584906,0.002321463,0.002516092,0.002944033,0.0002904459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110275,"about_ca_system_score_gemma":0.001859698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0119699,"about_ca_topic_score_gemma":0.01286877,"domain_scores_codex":[0.9862845,0.01058973,0.0004634712,0.001416959,0.0008502156,0.0003951922],"domain_scores_gemma":[0.917083,0.06802259,0.005223131,0.006927247,0.0021206,0.0006234881],"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":[0.0006134006,0.0003138929,0.08376195,0.0003864483,0.001046498,0.0005674789,0.001327955,0.562524,0.001277762,0.09454282,0.003035865,0.250602],"study_design_scores_gemma":[0.00006771568,0.0001361083,0.006981209,0.0001043341,0.0001871909,0.0001417794,0.000141339,0.9138721,0.000818096,0.07555223,0.001931268,0.00006673551],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04520948,0.0004320838,0.9528109,0.0004766737,0.00004132887,0.0001414859,0.0001741428,0.0002675983,0.0004464088],"genre_scores_gemma":[0.6381956,0.0007335305,0.3575244,0.0003955387,0.0001061262,0.0005261539,0.0007896649,0.0001329145,0.001596021],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03812851,"threshold_uncertainty_score":0.2016453,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320176121","doi":"10.1214/22-aoas1624","title":"Regularized fingerprinting in detection and attribution of climate change with weight matrix optimizing the efficiency in scaling factor estimation","year":2023,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"National Science Foundation","keywords":"Covariance matrix; Mathematics; Statistics; Estimator; Matrix (chemical analysis); Linear regression; Covariance; Estimation of covariance matrices; Applied mathematics","authors":[{"name":"Yan Li","is_ca":false},{"name":"Kun Chen","is_ca":false},{"name":"Jun Yan","is_ca":false},{"name":"Xuebin Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08214424114205639,"gpt":0.2889005372703241,"spread":0.2067562961282677,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007453694,0.001229687,0.001432173,0.001236281,0.0005834709,0.001389424,0.001831463,0.001495094,0.001556041],"category_scores_gemma":[0.04480987,0.000847202,0.001061762,0.001208929,0.002104425,0.002695688,0.002263754,0.002079136,0.0004591715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008521557,"about_ca_system_score_gemma":0.001806844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005643999,"about_ca_topic_score_gemma":0.003635316,"domain_scores_codex":[0.9970854,0.001475082,0.0001686676,0.0006212664,0.0004608681,0.0001887849],"domain_scores_gemma":[0.9813542,0.01306685,0.0013771,0.002333542,0.001557188,0.000311104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002242259,0.00008953143,0.003516536,0.0002065849,0.0001454799,0.0001472391,0.0001926148,0.8349603,0.005657946,0.05242708,0.001063813,0.1013685],"study_design_scores_gemma":[0.00001367569,0.00002248031,0.0003599947,0.00001529005,0.00001221768,0.00002185973,0.00001066413,0.9812284,0.0008656382,0.01714411,0.0002906219,0.00001504346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01556785,0.0001501107,0.9835604,0.0001381557,0.0000212245,0.00002692212,0.00003295394,0.0001667054,0.0003356916],"genre_scores_gemma":[0.4304358,0.0005375487,0.5657392,0.0001811586,0.0001325282,0.0002411493,0.000360181,0.0002095576,0.00216283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007453694,"threshold_uncertainty_score":0.03941935,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2602208471","doi":"10.1214/18-aoas1162","title":"Exact spike train inference via $\\ell_{0}$ optimization","year":2018,"lang":"en","type":"preprint","venue":"The Annals of Applied Statistics","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Python (programming language); Computer science; TRACE (psycholinguistics); Inference; Software; Optimization problem; Spike (software development); Convex optimization; Measure (data warehouse); Algorithm; Mathematical optimization; Regular polygon; Mathematics; Artificial intelligence; Programming language","authors":[{"name":"Sean Jewell","is_ca":false},{"name":"Daniela Witten","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1049976159803365,"gpt":0.3403495868487135,"spread":0.235351970868377,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002097449,0.001252734,0.001818462,0.0007176046,0.0006482018,0.001875449,0.002731105,0.001918352,0.01060766],"category_scores_gemma":[0.009949487,0.0009990442,0.001383936,0.001083609,0.001404134,0.002151957,0.002212428,0.003750151,0.003516207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736048,"about_ca_system_score_gemma":0.003775154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01336135,"about_ca_topic_score_gemma":0.01875402,"domain_scores_codex":[0.9991205,0.0003022323,0.00005409397,0.0002159638,0.0001843113,0.0001229244],"domain_scores_gemma":[0.9969837,0.002161008,0.0001471298,0.0003056713,0.0002605838,0.0001417767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000211859,0.00006615103,0.0008435741,0.0001275118,0.0000811912,0.00007455896,0.00004758436,0.8751196,0.001065049,0.03222102,0.01002408,0.0801179],"study_design_scores_gemma":[0.00001192635,0.000005634765,0.00004448575,0.000005345043,0.000003345598,0.0000072224,0.000003352717,0.9853083,0.0003164973,0.01368188,0.0006073921,0.000004533441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007388655,0.0002801189,0.9852889,0.0006226281,0.00007761726,0.00003655654,0.0003851224,0.003855958,0.002064424],"genre_scores_gemma":[0.2693916,0.0004854965,0.7116472,0.00115578,0.0002478563,0.000359339,0.00358253,0.002713658,0.01041663],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01336135,"threshold_uncertainty_score":0.03548616,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2990428857","doi":"10.1214/19-aoas1274","title":"Joint model of accelerated failure time and mechanistic nonlinear model for censored covariates, with application in HIV/AIDS","year":2019,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; City University of New York; National Science Foundation","keywords":"Covariate; Proportional hazards model; Accelerated failure time model; Econometrics; Inference; Statistics; Survival analysis; Computer science; Outcome (game theory); Mathematics; Artificial intelligence","authors":[{"name":"Hongbin Zhang","is_ca":true},{"name":"Lang Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08529347782029319,"gpt":0.3467134122240529,"spread":0.2614199344037597,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01180511,0.001344889,0.002019561,0.001467621,0.000756833,0.001607742,0.003663475,0.002272207,0.004167174],"category_scores_gemma":[0.0248914,0.001041275,0.002600108,0.001787747,0.002342518,0.002873084,0.002191789,0.003235599,0.0006743119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001702966,"about_ca_system_score_gemma":0.002224345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01016114,"about_ca_topic_score_gemma":0.007418862,"domain_scores_codex":[0.9958144,0.002596599,0.0001398635,0.0007069493,0.0004611585,0.0002810121],"domain_scores_gemma":[0.9873714,0.009418635,0.001424082,0.0007392115,0.0007432006,0.0003034469],"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":[0.000184916,0.00008512332,0.005823607,0.0002305283,0.0002268632,0.0004655421,0.0004247682,0.5268952,0.0007841106,0.4377263,0.001419737,0.02573344],"study_design_scores_gemma":[0.00003373089,0.00006981476,0.0008125363,0.00002334923,0.00006567872,0.000119519,0.00003183232,0.8978777,0.00015953,0.09891262,0.001864419,0.00002932397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01296107,0.000400695,0.9847964,0.0004184147,0.00005612848,0.00005861427,0.0002079258,0.0001388172,0.0009619687],"genre_scores_gemma":[0.5970339,0.002104139,0.3760354,0.0005755353,0.0003941456,0.001090569,0.001080818,0.0002310016,0.02145456],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01180511,"threshold_uncertainty_score":0.06243211,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107904222","doi":"10.1214/10-aoas398l","title":"Discussion of: A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?","year":2011,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","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 Guelph","funders":"","keywords":"Proxy (statistics); Autocorrelation; Statistical analysis; Paleoclimatology; Econometrics; History; Geology; Psychology; Statistics; Mathematics; Climate change; Oceanography","authors":[{"name":"Stephen McIntyre","is_ca":true},{"name":"Ross McKitrick","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03359346518537585,"gpt":0.2685703369750909,"spread":0.2349768717897151,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06718966,0.001122973,0.001813395,0.002468162,0.002373038,0.005923705,0.005250442,0.00883307,0.008092367],"category_scores_gemma":[0.2772078,0.0004816798,0.002499653,0.004704736,0.00909135,0.009556366,0.002650776,0.009903645,0.002130814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002715585,"about_ca_system_score_gemma":0.004311086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004741553,"about_ca_topic_score_gemma":0.003320259,"domain_scores_codex":[0.9671828,0.02247147,0.002280351,0.004134366,0.003582766,0.0003483187],"domain_scores_gemma":[0.7477626,0.2179083,0.009063208,0.008781556,0.01410568,0.002378765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002015025,0.00009035044,0.008224595,0.001236759,0.001049718,0.0008351497,0.0009209656,0.007430037,0.0007707033,0.3966815,0.4941617,0.08839698],"study_design_scores_gemma":[0.0000416037,0.00009112944,0.006449436,0.0006659693,0.0002096576,0.0009665526,0.0007650537,0.0161061,0.0007975634,0.6727262,0.3010252,0.0001555741],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003680706,0.01671165,0.1249747,0.8188745,0.03097975,0.00007064998,0.001143825,0.0001890572,0.003375181],"genre_scores_gemma":[0.2208916,0.02328632,0.1225546,0.3674247,0.2499527,0.0006282001,0.001286775,0.0008883293,0.01308685],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.06718966,"threshold_uncertainty_score":0.3553371,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401373758","doi":"10.1214/24-aoas1877","title":"Probabilistic contrastive dimension reduction for case-control study data","year":2024,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Canadian Institute for Advanced Research; National Cancer Institute; National Science Foundation; National Institutes of Health; National Institute of Environmental Health Sciences; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Dimensionality reduction; Dimension (graph theory); Reduction (mathematics); Probabilistic logic; Computer science; Statistics; Data reduction; Artificial intelligence; Natural language processing; Mathematics","authors":[{"name":"Didong Li","is_ca":false},{"name":"Andrew Jones","is_ca":false},{"name":"Barbara E. Engelhardt","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08856121255749513,"gpt":0.3719493295497239,"spread":0.2833881169922288,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04075399,0.001089864,0.001597415,0.002722795,0.001045747,0.001870676,0.002595002,0.001959879,0.001448242],"category_scores_gemma":[0.1160982,0.0009046886,0.002394829,0.002099454,0.00382221,0.001496007,0.002352076,0.003964915,0.0003355113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001673081,"about_ca_system_score_gemma":0.001877899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266629,"about_ca_topic_score_gemma":0.001822624,"domain_scores_codex":[0.9719257,0.02164065,0.0009270888,0.002969757,0.002326252,0.0002105169],"domain_scores_gemma":[0.9125114,0.0731402,0.003472788,0.008529861,0.001984606,0.0003611689],"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":[0.001119371,0.0003945045,0.02456483,0.001048868,0.001717549,0.0008310105,0.001001684,0.3031078,0.009804653,0.2836433,0.01037278,0.3623936],"study_design_scores_gemma":[0.0001322687,0.0001561858,0.005128036,0.00007972323,0.0001196484,0.0003577184,0.00004702397,0.7510155,0.00262396,0.2353159,0.004952166,0.00007181002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005045044,0.0003822597,0.9934002,0.0004080937,0.00003466155,0.0001743495,0.0001587341,0.0002083337,0.0001883516],"genre_scores_gemma":[0.2339985,0.0005284492,0.76179,0.0005510615,0.0002187273,0.001435867,0.0009172196,0.0001094668,0.000450689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04075399,"threshold_uncertainty_score":0.2155303,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3181775732","doi":"10.1214/20-aoas1423","title":"A compositional model to assess expression changes from single-cell RNA-seq data","year":2021,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","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":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Science Foundation; National Institutes of Health; National Cancer Institute; National Institute of Dental and Craniofacial Research; University of Wisconsin-Madison","keywords":"Cluster analysis; Bayesian probability; Expression (computer science); Mixture model; Computer science; Prior probability; Hierarchical clustering; Posterior probability; Probability distribution; Computational biology; Mathematics; Data mining; Artificial intelligence; Statistics; Biology","authors":[{"name":"Xiuyu Ma","is_ca":false},{"name":"Keegan Korthauer","is_ca":true},{"name":"Christina Kendziorski","is_ca":false},{"name":"Michael A. Newton","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2149168327828449,"gpt":0.3373738237349338,"spread":0.1224569909520889,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006643904,0.0008264289,0.001112105,0.001569165,0.0005632261,0.001531622,0.002120581,0.001334107,0.001912],"category_scores_gemma":[0.01769134,0.0006849523,0.001191453,0.001254038,0.001444116,0.002015372,0.001959325,0.001657651,0.0007557811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019839,"about_ca_system_score_gemma":0.001268869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003092163,"about_ca_topic_score_gemma":0.003823468,"domain_scores_codex":[0.9984067,0.000682727,0.00007695166,0.0003340107,0.0004034641,0.00009606958],"domain_scores_gemma":[0.9941158,0.004108523,0.000608767,0.0005610915,0.0004436174,0.0001621838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003459323,0.0001265428,0.008762974,0.000321548,0.0002247695,0.0002171536,0.0003826417,0.7137663,0.03716441,0.1530207,0.001417962,0.08424908],"study_design_scores_gemma":[0.000008958033,0.00003296915,0.000801675,0.00000891763,0.00001859016,0.00005064831,0.00001665778,0.9535383,0.001714074,0.04327321,0.0005166157,0.00001936107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01467098,0.00008960123,0.984447,0.00008964345,0.00001039462,0.00003879876,0.0001287372,0.0001715274,0.0003532879],"genre_scores_gemma":[0.4151409,0.000542243,0.5787247,0.0003138543,0.00009986036,0.0005512266,0.001362901,0.0002607729,0.003003408],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006643904,"threshold_uncertainty_score":0.03513676,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4367598648","doi":"10.1214/22-aoas1675","title":"Mixed-frequency extreme value regression: Estimating the effect of mesoscale convective systems on extreme rainfall intensity","year":2023,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation HEC","keywords":"Extreme value theory; Flash flood; Mesoscale meteorology; Generalized extreme value distribution; Maxima; Environmental science; Climatology; Intensity (physics); Covariate; Statistics; Mesoscale convective system; Regression analysis; Meteorology; Flood myth; Geography; Mathematics; Geology","authors":[{"name":"Debbie J. Dupuis","is_ca":true},{"name":"Luca Trapin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06433382728355574,"gpt":0.2969347383230752,"spread":0.2326009110395194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006658774,0.0006825888,0.0007746517,0.001211411,0.000367383,0.0008135073,0.00129107,0.0008399388,0.001580149],"category_scores_gemma":[0.02286596,0.0003896522,0.001183032,0.001179633,0.0005125075,0.0008803001,0.0008086163,0.0009659399,0.0003469407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003441948,"about_ca_system_score_gemma":0.0005597728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0072029,"about_ca_topic_score_gemma":0.005240914,"domain_scores_codex":[0.9969581,0.002122306,0.00008615766,0.0005329536,0.0001402013,0.0001601659],"domain_scores_gemma":[0.9826977,0.01360583,0.001569233,0.001379186,0.0004552153,0.0002929347],"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":[0.0008461032,0.0005082245,0.6170397,0.00009351942,0.001527732,0.0003597041,0.0002747625,0.2593668,0.002624482,0.01000142,0.002028529,0.1053291],"study_design_scores_gemma":[0.00003127765,0.0002808115,0.06395778,0.00001843366,0.0001087953,0.000111127,0.00009276663,0.92777,0.000861726,0.005840327,0.0008971409,0.00002980107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.697387,0.0003123823,0.3000457,0.0003087162,0.00004351177,0.00007682193,0.000797452,0.0003724897,0.0006559649],"genre_scores_gemma":[0.9557275,0.0001028209,0.04245623,0.00004539581,0.00006112479,0.00009456382,0.000728899,0.0000318132,0.0007516775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0072029,"threshold_uncertainty_score":0.03521538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2983854231","doi":"10.1214/21-aoas1565","title":"Measurement error correction in particle tracking microrheology","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Streptococcal Infections and Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Microrheology; Estimator; Range (aeronautics); Computer science; Power law; Bandwidth (computing); Viscoelasticity; Algorithm; Statistical physics; Physics; Acoustics; Mathematics; Statistics; Engineering; Telecommunications; Aerospace engineering","authors":[{"name":"Yun Ling","is_ca":true},{"name":"Martin Lysy","is_ca":true},{"name":"Ian Seim","is_ca":false},{"name":"Jay Newby","is_ca":true},{"name":"David B. Hill","is_ca":false},{"name":"Jeremy Cribb","is_ca":false},{"name":"M. Gregory Forest","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.151902174140617,"gpt":0.3692182806897776,"spread":0.2173161065491606,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005665807,0.001019125,0.001008689,0.0009460809,0.0006135853,0.0008167314,0.001233597,0.001521303,0.0008458847],"category_scores_gemma":[0.02418296,0.0004732715,0.0006313093,0.001108002,0.001154169,0.0009959642,0.001227402,0.00155931,0.0004915105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000684641,"about_ca_system_score_gemma":0.001623867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003289576,"about_ca_topic_score_gemma":0.002937318,"domain_scores_codex":[0.9977517,0.0008679257,0.0001542278,0.0004057058,0.0007264229,0.00009405063],"domain_scores_gemma":[0.991312,0.006483427,0.0005766279,0.0007280964,0.0008114597,0.00008841859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005129846,0.000109812,0.004282475,0.0005796224,0.0002363073,0.0002718363,0.0003752435,0.5004876,0.0311716,0.06617672,0.002787937,0.3930078],"study_design_scores_gemma":[0.00001797055,0.0000818593,0.0008487044,0.00003961603,0.00002709273,0.0001275384,0.00002352438,0.9698821,0.01348385,0.0120734,0.003361371,0.00003295417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003518353,0.0002664089,0.9956911,0.00008526901,0.00005512147,0.00001701423,0.00001784175,0.0001681658,0.0001807839],"genre_scores_gemma":[0.207775,0.001000125,0.7880993,0.0002074522,0.0001794569,0.0002069627,0.0002160985,0.0002622677,0.002053355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005665807,"threshold_uncertainty_score":0.02996403,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2761069967","doi":"10.1214/17-aoas1044","title":"Latent class modeling using matrix covariates with application to identifying early placebo responders based on EEG signals","year":2017,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Mental Health Research Topics","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Institute of Mental Health","keywords":"Covariate; Latent class model; Latent variable; Statistics; Placebo; Missing data; Bayesian probability; Matrix decomposition; Artificial intelligence; Computer science; Econometrics; Psychology; Mathematics; Medicine","authors":[{"name":"Bei Jiang","is_ca":true},{"name":"Eva Petkova","is_ca":false},{"name":"Thaddeus Tarpey","is_ca":false},{"name":"R. Todd Ogden","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2593056180934468,"gpt":0.4945810377768523,"spread":0.2352754196834055,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008829072,0.001218866,0.001513693,0.001348722,0.0006984267,0.0014992,0.001533739,0.001555588,0.003322643],"category_scores_gemma":[0.02148213,0.0005536099,0.001908846,0.00156198,0.001311034,0.001735517,0.00180819,0.00295184,0.0007717163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007029856,"about_ca_system_score_gemma":0.001686079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004143894,"about_ca_topic_score_gemma":0.003923295,"domain_scores_codex":[0.995749,0.002898299,0.0001140626,0.0006440866,0.0003862042,0.0002082327],"domain_scores_gemma":[0.9876188,0.009817757,0.0009191533,0.000792656,0.0006174801,0.0002340956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008779685,0.0004105387,0.01445977,0.00042464,0.0005131113,0.0004727485,0.001047962,0.4721923,0.006305649,0.1901927,0.004721247,0.3083814],"study_design_scores_gemma":[0.00002850812,0.00008088174,0.0009771802,0.00002353151,0.00002519201,0.00005345457,0.00003463833,0.9576948,0.0003700178,0.03941254,0.001273851,0.00002533018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007648695,0.00025166,0.9912327,0.0002433157,0.00002206282,0.00006236428,0.00009914141,0.0001794936,0.0002605691],"genre_scores_gemma":[0.4016207,0.001306173,0.5906352,0.000333676,0.000304945,0.0007683183,0.0009701118,0.0002412097,0.0038198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008829072,"threshold_uncertainty_score":0.04669315,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408585636","doi":"10.1214/24-aoas1958","title":"Poisson cluster process models for detecting ultra-diffuse galaxies","year":2025,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Computer science; Cluster (spacecraft); Poisson distribution; Statistical physics; Statistics; Mathematics; Physics","authors":[{"name":"Dayi Li","is_ca":true},{"name":"Alex Stringer","is_ca":true},{"name":"Gwendolyn M. Eadie","is_ca":true},{"name":"Roberto Abraham","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05633146807322464,"gpt":0.3493496021781208,"spread":0.2930181341048962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005721026,0.001173585,0.001474424,0.002015111,0.0007916148,0.001702985,0.005781434,0.001693214,0.00204738],"category_scores_gemma":[0.01559854,0.0009404047,0.001736518,0.001363027,0.002109669,0.002123845,0.002033018,0.00252122,0.0006257968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166143,"about_ca_system_score_gemma":0.001593049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01517748,"about_ca_topic_score_gemma":0.009321512,"domain_scores_codex":[0.9981364,0.000787377,0.00006926136,0.0004960323,0.0003412195,0.0001696498],"domain_scores_gemma":[0.989108,0.00731562,0.0013437,0.0007887702,0.001062562,0.0003813501],"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":[0.0001349949,0.00005426866,0.0116968,0.00008251445,0.0001394521,0.0001373954,0.0001533976,0.8848391,0.0007116627,0.07686722,0.001432705,0.02375054],"study_design_scores_gemma":[0.00001097585,0.00001298117,0.000405543,0.000005882787,0.00001412066,0.00002099563,0.000009865052,0.9826205,0.0001641986,0.01642569,0.000297502,0.00001167308],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03002056,0.000267264,0.9678704,0.0003812776,0.00004510278,0.00007420069,0.0002320556,0.0002976511,0.0008115672],"genre_scores_gemma":[0.7515829,0.0008367721,0.2401448,0.0004329763,0.0002809713,0.0005196318,0.001424177,0.0001539907,0.004623748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01517748,"threshold_uncertainty_score":0.03025609,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391418111","doi":"10.1214/23-aoas1795","title":"Network-level traffic flow prediction: Functional time series vs. functional neural network approach","year":2024,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Traffic Prediction and Management Techniques","field":"Engineering","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 Toronto","funders":"","keywords":"Time series; Computer science; Series (stratigraphy); Artificial neural network; Traffic flow (computer networking); Artificial intelligence; Machine learning; Geology; Computer network","authors":[{"name":"Tao Ma","is_ca":false},{"name":"Fang Yao","is_ca":false},{"name":"Zhou Zhou","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04532785483451583,"gpt":0.2309017812429498,"spread":0.185573926408434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001603169,0.001132102,0.0006795155,0.0009055315,0.0002504366,0.0007784943,0.001457339,0.001011372,0.0009507082],"category_scores_gemma":[0.003875378,0.0002929723,0.0005628341,0.001105065,0.0005762049,0.001977769,0.0006598177,0.001148254,0.0001604903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008808265,"about_ca_system_score_gemma":0.0005998886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01479876,"about_ca_topic_score_gemma":0.007145651,"domain_scores_codex":[0.9995084,0.0001872047,0.00002479968,0.0001478433,0.00008003183,0.00005172539],"domain_scores_gemma":[0.9984592,0.0008998669,0.0001840494,0.0001109547,0.0002789243,0.00006707077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005005248,0.00003874254,0.002670714,0.00003909502,0.00003736765,0.00004170126,0.00002352385,0.9654554,0.0002924288,0.008687883,0.0004404959,0.02222264],"study_design_scores_gemma":[4.580639e-7,0.000003660297,0.0001453534,0.000001487426,0.000002465734,0.000002250433,0.000002424811,0.9986116,0.00002977704,0.001157275,0.00004184596,0.000001419593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09252863,0.001093265,0.9019942,0.0007637327,0.0001177005,0.00003052717,0.000311842,0.0003094165,0.002850702],"genre_scores_gemma":[0.9693117,0.0008376457,0.02769158,0.0001077392,0.0001624056,0.00005185674,0.0004655183,0.00003547483,0.001336194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01479876,"threshold_uncertainty_score":0.02942526,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4287780226","doi":"10.1214/22-aoas1701","title":"The scalable birth–death MCMC algorithm for mixed graphical model learning with application to genomic data integration","year":2023,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","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 Toronto; York University; Lunenfeld-Tanenbaum Research Institute; University of New Brunswick","funders":"National Center for Advancing Translational Sciences; National Cancer Institute","keywords":"Subtyping; Lasso (programming language); Computer science; Inference; Scalability; Biological data; Feature selection; Computational biology; Data mining; Machine learning; Artificial intelligence; Bioinformatics; Biology","authors":[{"name":"Nanwei Wang","is_ca":true},{"name":"Hélène Massam","is_ca":true},{"name":"Xin Gao","is_ca":true},{"name":"Laurent Briollais","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.062091907064135,"gpt":0.3370713860687274,"spread":0.2749794790045924,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005941883,0.001380104,0.001929703,0.001933784,0.001289989,0.001565032,0.003856547,0.002363866,0.003876569],"category_scores_gemma":[0.01918528,0.001337328,0.002043894,0.002088329,0.001408291,0.001238602,0.003007125,0.004579017,0.001031838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001808824,"about_ca_system_score_gemma":0.003614931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01847696,"about_ca_topic_score_gemma":0.01888968,"domain_scores_codex":[0.9976112,0.001429433,0.0001033531,0.0002986391,0.0004214431,0.0001358956],"domain_scores_gemma":[0.9877264,0.009960955,0.0004829379,0.0005245602,0.0009426751,0.0003624695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001667714,0.00007823791,0.002345412,0.0001564877,0.0002379515,0.0001895109,0.0001353782,0.860866,0.0009487717,0.04254412,0.003891507,0.08843993],"study_design_scores_gemma":[0.00001312244,0.000006912626,0.00004300918,0.00000417424,0.000004983115,0.000009405331,0.000002745421,0.992645,0.00008251319,0.006857586,0.0003253851,0.000005056343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002281914,0.0002132083,0.996349,0.0002435668,0.00003225165,0.00006386393,0.00008332733,0.0004651525,0.000267602],"genre_scores_gemma":[0.1163471,0.0003950732,0.8784335,0.0005092238,0.0001565821,0.0009465545,0.0009432797,0.000341972,0.001926725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01847696,"threshold_uncertainty_score":0.03673881,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3021847850","doi":"10.1214/21-aoas1526","title":"Permutation tests under a rotating sampling plan with clustered data","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Forest ecology and management","field":"Environmental 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 British Columbia","funders":"","keywords":"Permutation (music); Sampling (signal processing); Resampling; Statistics; Parametric statistics; Inference; Econometrics; Sampling distribution; Computer science; Mathematics; Artificial intelligence","authors":[{"name":"Jiahua Chen","is_ca":true},{"name":"Yukun Liu","is_ca":false},{"name":"Carilyn G. Taylor","is_ca":true},{"name":"James V. Zidek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1241012765550882,"gpt":0.3257595050175824,"spread":0.2016582284624941,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09458126,0.001749963,0.002817704,0.002358214,0.001950551,0.002651979,0.00669221,0.003017192,0.005697138],"category_scores_gemma":[0.3216661,0.001249252,0.00362089,0.004074985,0.006351468,0.005143799,0.002987018,0.004366833,0.001144502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002085283,"about_ca_system_score_gemma":0.004044478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003483183,"about_ca_topic_score_gemma":0.001988039,"domain_scores_codex":[0.8424625,0.1181787,0.005025911,0.02235782,0.009633167,0.002341961],"domain_scores_gemma":[0.6207936,0.2715055,0.02990236,0.06574157,0.01001584,0.002041241],"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":[0.005126791,0.0009910834,0.06084585,0.0007100078,0.003074319,0.00204525,0.002168668,0.243663,0.006474625,0.3692274,0.00647503,0.2991981],"study_design_scores_gemma":[0.0006933592,0.00213053,0.01450444,0.00009619241,0.0003608525,0.0004685061,0.0004055783,0.8398683,0.003308558,0.1350932,0.002900104,0.0001704052],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07290731,0.0001094887,0.9230503,0.0003583087,0.0001143364,0.000842845,0.0005373936,0.0006414147,0.001438667],"genre_scores_gemma":[0.5067963,0.0001409772,0.4862734,0.0003845307,0.0001721444,0.00284942,0.001492944,0.0001545943,0.001735774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09458126,"threshold_uncertainty_score":0.5001995,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408584779","doi":"10.1214/24-aoas1964","title":"A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions","year":2025,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"McGill University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Outbreak; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Markov chain; Markov model; Statistics; State (computer science); Geography; Computer science; Econometrics; Virology; Medicine; Mathematics; Algorithm; Infectious disease (medical specialty)","authors":[{"name":"Dirk Douwes‐Schultz","is_ca":true},{"name":"Alexandra M. Schmidt","is_ca":true},{"name":"Yan Shen","is_ca":true},{"name":"David L. Buckeridge","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2507333540244364,"gpt":0.4762858184953547,"spread":0.2255524644709184,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282975,0.0006805073,0.000865703,0.001182321,0.001054042,0.001598046,0.002724385,0.001497698,0.006316734],"category_scores_gemma":[0.002897862,0.0005557304,0.00106305,0.001148937,0.001102024,0.000768832,0.0008936513,0.001461289,0.0004340169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008859769,"about_ca_system_score_gemma":0.005619479,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7975352,"about_ca_topic_score_gemma":0.7121315,"domain_scores_codex":[0.9993532,0.0001858276,0.00002626738,0.0001752974,0.00005926564,0.0002002431],"domain_scores_gemma":[0.9982696,0.0008939226,0.0002965299,0.00007140537,0.0003014693,0.0001670712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000205629,0.000117224,0.03257948,0.00006593994,0.0001580409,0.0005037874,0.0003250451,0.9082054,0.001299511,0.04250427,0.003885863,0.01014987],"study_design_scores_gemma":[0.00001977438,0.00001477725,0.004019934,0.000008595772,0.00002840187,0.0000197123,0.00004710191,0.9923446,0.00004375525,0.002784903,0.0006504726,0.000018043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.703081,0.001209259,0.2642353,0.004989809,0.0001895722,0.0003237848,0.01024042,0.0006158276,0.01511503],"genre_scores_gemma":[0.9745786,0.0003674995,0.01139297,0.000174694,0.00005060354,0.0001315,0.002357421,0.00003524226,0.01091153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2024648,"threshold_uncertainty_score":0.4073142,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}