{"meta":{"query_hash":"55aac7c98b92","filters":{"venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)"},"cohort_total":98,"direct_labels_cover":0,"predictions_cover":98,"exported":98,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/55aac7c98b92","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+the+Royal+Statistical+Society+Series+C+%28Applied+Statistics%29"},"results":[{"id":"W1790537050","doi":"10.1111/j.1467-9876.2012.01040.x","title":"Testing Quantum States for Purity","year":2012,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea","keywords":"Mathematics; Statistic; Poisson distribution; Statistics; Quantum state; Deviance (statistics); Quantum; Statistical physics; Quantum mechanics; Physics","score_opus":0.013699039208075536,"score_gpt":0.24177908614887977,"score_spread":0.22808004694080425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1790537050","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66637945,0.00037120716,0.31102785,0.0026594475,0.00013399866,0.00010841085,0.00077069155,0.000367787,0.018181216],"genre_scores_gemma":[0.98840815,0.00004301844,0.010890695,0.00013242186,0.00006545768,0.00004443418,0.00013087312,0.000031634107,0.00025321927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98407596,0.00908748,0.000773782,0.002045748,0.0033840192,0.0006329338],"domain_scores_gemma":[0.8039443,0.16061547,0.008713417,0.01595827,0.0074786055,0.0032898663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013646145,0.00052083615,0.0016650391,0.0030155757,0.0017668525,0.003912561,0.0016548281,0.0016456015,0.005538691],"category_scores_gemma":[0.10671062,0.0003069159,0.0010101161,0.0021670652,0.013110008,0.00827258,0.0050461753,0.0034738637,0.00039714173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050156907,0.00015701473,0.021681014,0.00015054147,0.00018246782,0.0001476917,0.0006081591,0.021519775,0.0037649227,0.91770726,0.0011886714,0.032390915],"study_design_scores_gemma":[0.00003683402,0.00018514361,0.004831971,0.00004455323,0.000034820645,0.00012365411,0.00033244857,0.073018454,0.003246844,0.9170346,0.0010490781,0.00006162317],"about_ca_topic_score_codex":0.00066190585,"about_ca_topic_score_gemma":0.0002620731,"teacher_disagreement_score":0.013646145,"about_ca_system_score_codex":0.0011916237,"about_ca_system_score_gemma":0.0012708749,"threshold_uncertainty_score":0.07216853},"labels":[],"label_agreement":null},{"id":"W1853339616","doi":"10.1111/rssc.12124","title":"A General Angular Regression Model for the Analysis of Data on Animal Movement in Ecology","year":2015,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Parks Canada","keywords":"Estimator; Identifiability; Statistics; Covariance; Mathematics; Regression; Variance (accounting); Regression analysis; Displacement (psychology); Data set; Set (abstract data type); Ecology; Econometrics; Applied mathematics; Computer science; Biology","score_opus":0.0366326648801705,"score_gpt":0.28769649821201027,"score_spread":0.2510638333318398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1853339616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009684137,0.00047884457,0.98666686,0.00042360416,0.00006669795,0.000054323496,0.0006743527,0.00035657233,0.0015946173],"genre_scores_gemma":[0.49374434,0.0030188144,0.46552935,0.00058090664,0.00053351186,0.0013478491,0.004291533,0.0007199234,0.030233735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942121,0.0034752905,0.00029088606,0.0011476708,0.00054674875,0.00032736655],"domain_scores_gemma":[0.9893991,0.007505632,0.000973775,0.0011421014,0.00080807577,0.0001713857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011327038,0.001390307,0.0014054585,0.0019385288,0.00047937236,0.0019590764,0.0029760397,0.0018016453,0.008051344],"category_scores_gemma":[0.025721176,0.0009368454,0.002213936,0.004229529,0.0014491922,0.0025649825,0.0018341831,0.002540739,0.0034441918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021940023,0.000105103754,0.0067631733,0.00025857257,0.00029208604,0.00032252958,0.00032514712,0.7034873,0.001954788,0.22239988,0.004612842,0.059259225],"study_design_scores_gemma":[0.00001948995,0.000071959854,0.001755498,0.000033371638,0.000031313477,0.000091881346,0.000041887677,0.9319336,0.00015161811,0.0609501,0.0048828516,0.00003640403],"about_ca_topic_score_codex":0.015251342,"about_ca_topic_score_gemma":0.011622016,"teacher_disagreement_score":0.015251342,"about_ca_system_score_codex":0.0015646733,"about_ca_system_score_gemma":0.0015096075,"threshold_uncertainty_score":0.05990386},"labels":[],"label_agreement":null},{"id":"W1895369562","doi":"10.1111/rssc.12122","title":"Comparing Two Binary Diagnostic Tests with Repeated Measurements","year":2015,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Malaria Research and Control","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Johns Hopkins University","keywords":"Statistics; Binary data; Estimator; Mathematics; Binary number; Correlation; Gold standard (test); Maximum likelihood; Population; Medicine; Arithmetic","score_opus":0.04446025806292636,"score_gpt":0.2959683326146744,"score_spread":0.25150807455174806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1895369562","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59871954,0.006417652,0.37394872,0.0052587967,0.0026382273,0.00078613654,0.005774584,0.0008032441,0.0056531164],"genre_scores_gemma":[0.95053434,0.00024444525,0.04573178,0.0007294253,0.00039358548,0.00048038672,0.0010935975,0.00006868663,0.0007238369],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8586517,0.10790681,0.0058721034,0.012658301,0.013227034,0.0016840217],"domain_scores_gemma":[0.536423,0.39067602,0.035247564,0.030779425,0.0054619755,0.0014119276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07615445,0.0010104809,0.0028322064,0.0026282256,0.0005654365,0.0024358425,0.0035705785,0.0030641346,0.005995],"category_scores_gemma":[0.32771212,0.000558464,0.003025163,0.0019895476,0.0037806842,0.0022496455,0.002121297,0.0032583494,0.0007049325],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.024279919,0.0016983598,0.445002,0.004945661,0.032907285,0.002599156,0.0020764426,0.082351804,0.00929043,0.08392225,0.018253583,0.29267323],"study_design_scores_gemma":[0.0019752895,0.013649829,0.31322607,0.0016621513,0.008314867,0.0044680517,0.0011774071,0.28580275,0.015379298,0.33590478,0.017636731,0.0008027504],"about_ca_topic_score_codex":0.00094146666,"about_ca_topic_score_gemma":0.00055183395,"teacher_disagreement_score":0.07615445,"about_ca_system_score_codex":0.0016640075,"about_ca_system_score_gemma":0.00089326734,"threshold_uncertainty_score":0.40274805},"labels":[],"label_agreement":null},{"id":"W1965933313","doi":"10.1111/j.1467-9876.2007.00608.x","title":"Modelling Mercury Deposition Through Latent Space–Time Processes","year":2008,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of Environmental Health Sciences; Office of Research and Development; U.S. Environmental Protection Agency","keywords":"Deposition (geology); Environmental science; Precipitation; Meteorology; Interpolation (computer graphics); Mercury (programming language); Atmospheric sciences; Computer science; Hydrology (agriculture); Geology; Geography; Artificial intelligence","score_opus":0.02877644499352748,"score_gpt":0.25788509321186576,"score_spread":0.22910864821833826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965933313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10587213,0.00032429735,0.8878803,0.00089983986,0.00006486758,0.00005658595,0.0013618954,0.00045117523,0.003088834],"genre_scores_gemma":[0.9211084,0.0008101475,0.06384952,0.000112551716,0.0001486865,0.00028145942,0.0014072072,0.00012953993,0.012152469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989711,0.0004085606,0.000054557513,0.00026781257,0.00015963128,0.0001384254],"domain_scores_gemma":[0.9977011,0.0014908243,0.0004043628,0.00017082664,0.00015119613,0.000081650745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021085145,0.000649827,0.00070562446,0.001112069,0.0005264352,0.0021989369,0.0018963784,0.0017203852,0.0033763398],"category_scores_gemma":[0.005650138,0.00079501554,0.0014098629,0.0016974846,0.0012723184,0.002795884,0.0015892999,0.0018308833,0.00067532103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003483319,0.000037213184,0.0036086214,0.000023408209,0.000046393492,0.000068701476,0.000112346,0.9271947,0.0004363387,0.06485782,0.000350972,0.0032285925],"study_design_scores_gemma":[0.000012693885,0.000009254703,0.00035809324,0.000003899433,0.000009623386,0.000014050903,0.000012269168,0.97944987,0.00009243223,0.019459546,0.00057117257,0.000007132423],"about_ca_topic_score_codex":0.024592696,"about_ca_topic_score_gemma":0.020589773,"teacher_disagreement_score":0.024592696,"about_ca_system_score_codex":0.0016785014,"about_ca_system_score_gemma":0.0013957456,"threshold_uncertainty_score":0.048899114},"labels":[],"label_agreement":null},{"id":"W1968707191","doi":"10.1111/j.1467-9876.2008.00652.x","title":"Using Bayesian Inference to Understand the Allocation of Resources Between Sexual and Asexual Reproduction","year":2009,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Markov chain Monte Carlo; Bayesian inference; Computer science; Bayesian probability; Statistical inference; Prior probability; Range (aeronautics); Artificial intelligence; Machine learning; Ecology; Mathematics; Statistics; Biology; Engineering","score_opus":0.038935682927281896,"score_gpt":0.277261171259499,"score_spread":0.23832548833221712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968707191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033925135,0.00017821057,0.96412855,0.000593916,0.000014090189,0.000016998605,0.00009737959,0.00008647065,0.00095933565],"genre_scores_gemma":[0.70954496,0.00055530923,0.28736076,0.00026497207,0.00012164637,0.00013344195,0.00029477317,0.00012483053,0.001599321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983381,0.001093068,0.00007617499,0.00022164498,0.00019427288,0.000076808836],"domain_scores_gemma":[0.98250073,0.015086999,0.0010118509,0.00066368806,0.00046030938,0.00027648933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008509505,0.0003363334,0.00083570916,0.0013224637,0.000566007,0.0014522666,0.0014762804,0.00085724046,0.0030061032],"category_scores_gemma":[0.032768562,0.0005962694,0.0008588724,0.00071420055,0.002148789,0.0031274743,0.0013342217,0.0016482986,0.00027407525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005668768,0.00004523943,0.005936939,0.00008782053,0.00012736533,0.000113096154,0.00023732787,0.59169585,0.0011095041,0.37215278,0.0009786574,0.027458737],"study_design_scores_gemma":[0.000008574388,0.0000059109557,0.000737058,0.000012777666,0.000008553766,0.000019921303,0.000015085521,0.70684373,0.000117740165,0.29183525,0.00038254703,0.000012867595],"about_ca_topic_score_codex":0.006559394,"about_ca_topic_score_gemma":0.006584087,"teacher_disagreement_score":0.008509505,"about_ca_system_score_codex":0.0013081341,"about_ca_system_score_gemma":0.0013939828,"threshold_uncertainty_score":0.045003057},"labels":[],"label_agreement":null},{"id":"W1970825665","doi":"10.1111/1467-9876.00406","title":"A Bounded Influence Regression Estimator Based on the Statistics of the Hat Matrix","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Woods Hole Oceanographic Institution; U.S. Department of Energy","keywords":"Estimator; Mathematics; Statistics; Leverage (statistics); Outlier; Quantile regression; Quantile; Bounded function; Scatter matrix; Robust regression; Diagonal; Matrix (chemical analysis); Estimation of covariance matrices; Mathematical analysis","score_opus":0.03329219515005504,"score_gpt":0.3522790135157561,"score_spread":0.31898681836570103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970825665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045291665,0.000095986936,0.99485445,0.00003754886,0.000016130185,0.000014289176,0.000025999589,0.00017275903,0.00025364355],"genre_scores_gemma":[0.3171498,0.00047638945,0.6793919,0.00014219417,0.00017969473,0.00020318652,0.0004938503,0.00020833945,0.0017546827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974775,0.0011879335,0.0001306274,0.0004693921,0.0006114649,0.00012304132],"domain_scores_gemma":[0.98535514,0.010397576,0.0012057328,0.001070534,0.0017391653,0.0002318606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052608103,0.00073126325,0.0011457108,0.0014381026,0.0003870261,0.0010339346,0.0014706217,0.0009738596,0.0015968416],"category_scores_gemma":[0.029452598,0.00044617566,0.00088587706,0.0011078365,0.0013605374,0.0013817219,0.0016383264,0.0015326974,0.00088083703],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031456814,0.00013357501,0.012439868,0.0002749157,0.0003972748,0.00032953348,0.00023639094,0.43253705,0.018959519,0.118199624,0.0057332506,0.41044438],"study_design_scores_gemma":[0.000015841088,0.000050733695,0.001663816,0.000024886422,0.000026875914,0.000074429394,0.000015082565,0.97313863,0.0036462445,0.01951933,0.0017964569,0.000027568836],"about_ca_topic_score_codex":0.0024920176,"about_ca_topic_score_gemma":0.0017640961,"teacher_disagreement_score":0.0052608103,"about_ca_system_score_codex":0.00054155174,"about_ca_system_score_gemma":0.0012461008,"threshold_uncertainty_score":0.027822137},"labels":[],"label_agreement":null},{"id":"W1974581996","doi":"10.1111/1467-9876.00400","title":"Regression Models for Cyclic Data","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Clockwise; Orientation (vector space); Logistic regression; Sine; Event (particle physics); Statistics; Mathematics; Probability distribution; Statistical physics; Geometry; Physics; Rotation (mathematics)","score_opus":0.033993234583456364,"score_gpt":0.28209683278284714,"score_spread":0.24810359819939076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974581996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034300577,0.002638349,0.95168537,0.00209082,0.00020998594,0.00016711517,0.003754858,0.0019109712,0.003242043],"genre_scores_gemma":[0.70632404,0.005041108,0.25040868,0.0007362184,0.00085657975,0.0017934513,0.01231092,0.0010604862,0.021468477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99482614,0.0030380958,0.00029074724,0.0011131114,0.00040071417,0.00033107743],"domain_scores_gemma":[0.96384585,0.029796308,0.0026239564,0.0016956276,0.0017387277,0.0002994531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012049782,0.0017737072,0.0017975423,0.0026331185,0.00060548366,0.0021748615,0.0036134871,0.002572288,0.0085303495],"category_scores_gemma":[0.051364742,0.0009466959,0.001938596,0.0043737376,0.0012802321,0.0029922796,0.0015842641,0.0031989887,0.0026052475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018436245,0.000092793125,0.007052849,0.0003836753,0.00039757756,0.00024144434,0.00030568553,0.8034515,0.00053882686,0.11524588,0.007936657,0.06416878],"study_design_scores_gemma":[0.00001441556,0.000021940976,0.0006721873,0.000025388292,0.000023219285,0.000038982154,0.000023661827,0.95340455,0.00007078866,0.04356546,0.0021190047,0.000020422081],"about_ca_topic_score_codex":0.017937737,"about_ca_topic_score_gemma":0.011973541,"teacher_disagreement_score":0.017937737,"about_ca_system_score_codex":0.001960827,"about_ca_system_score_gemma":0.0012539213,"threshold_uncertainty_score":0.06372607},"labels":[],"label_agreement":null},{"id":"W1996313199","doi":"10.1111/1467-9876.00198","title":"Estimating the Propagation Rate of a Viral Infection of Potato Plants via Mixtures of Regressions","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Bootstrapping (finance); Mathematics; Parametric statistics; Covariance matrix; Inference; Statistics; Matrix (chemical analysis); Confidence interval; Applied mathematics; Statistical inference; Fisher information; Linear regression; Econometrics; Computer science; Artificial intelligence; Biology","score_opus":0.027101616158653783,"score_gpt":0.33684696873310244,"score_spread":0.30974535257444863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996313199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2494474,0.00022704668,0.7486564,0.000120710836,0.00001812588,0.000047303874,0.00024490905,0.00073955284,0.000498592],"genre_scores_gemma":[0.8036722,0.00018692297,0.19420516,0.000026684029,0.000033525044,0.00006960796,0.0006442738,0.00014827366,0.0010132941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99628204,0.0021997346,0.00019645729,0.00077806803,0.00041079876,0.00013291254],"domain_scores_gemma":[0.95916384,0.03328458,0.0036083823,0.0023761953,0.0012371852,0.0003298979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009544368,0.0009086555,0.0011140927,0.0027424372,0.0002914698,0.0014590438,0.0012548126,0.0012481147,0.0013208651],"category_scores_gemma":[0.04268791,0.0010164343,0.0013854686,0.0012482402,0.00071871607,0.002343801,0.0014068701,0.0019102377,0.00060571765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010371556,0.0001797082,0.08581975,0.00018148404,0.0005873962,0.00023019248,0.00043678295,0.73563707,0.025047228,0.016065512,0.0007223086,0.13405538],"study_design_scores_gemma":[0.0000076008564,0.000055395667,0.006730796,0.0000127042385,0.000031563228,0.000065895874,0.000023968567,0.9840666,0.002666973,0.006042184,0.00025935055,0.000036976046],"about_ca_topic_score_codex":0.0029018342,"about_ca_topic_score_gemma":0.0016225366,"teacher_disagreement_score":0.009544368,"about_ca_system_score_codex":0.00055934774,"about_ca_system_score_gemma":0.00032176523,"threshold_uncertainty_score":0.050476074},"labels":[],"label_agreement":null},{"id":"W1996600093","doi":"10.1046/j.1467-9876.2003.05215.x","title":"Bayesian Analysis of Directed Graphs Data with Applications to Social Networks","year":2004,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Okanagan University College; Okanagan College","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Versailles Saint-Quentin-en-Yvelines","keywords":"Computer science; Variety (cybernetics); Markov chain Monte Carlo; Variable-order Bayesian network; Inference; Bayesian probability; Data mining; Theoretical computer science; Markov chain; Machine learning; Bayesian network; Covariate; Bayesian inference; Artificial intelligence; Data science","score_opus":0.010144831962567056,"score_gpt":0.26677348120998806,"score_spread":0.256628649247421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996600093","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011386865,0.00040463908,0.9844977,0.000529624,0.000029022536,0.00006668562,0.0013036847,0.0003645027,0.0014172628],"genre_scores_gemma":[0.34200627,0.0016673217,0.6471071,0.00035069586,0.00022712721,0.0006674876,0.004189722,0.00029414543,0.0034901334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99391174,0.004237459,0.0002180476,0.0005951487,0.00090757734,0.000130085],"domain_scores_gemma":[0.95416874,0.037950467,0.0020050055,0.0031319493,0.002331537,0.00041231915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007942573,0.00055609125,0.0010758169,0.0028855214,0.0007344105,0.001776636,0.0012484768,0.0007934588,0.0041773077],"category_scores_gemma":[0.047360945,0.0005216955,0.0008964869,0.0031840405,0.000904542,0.0023010548,0.001967387,0.0016578716,0.00081191154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021507152,0.00014463186,0.007475208,0.0003763943,0.00033589307,0.00024209269,0.00045466988,0.33685753,0.0023063412,0.3966922,0.011845993,0.243054],"study_design_scores_gemma":[0.000015339396,0.000024345756,0.0018724833,0.000053537402,0.000024345081,0.00008563366,0.00005617605,0.5997965,0.00049177126,0.39084348,0.0067018834,0.000034460754],"about_ca_topic_score_codex":0.0049869097,"about_ca_topic_score_gemma":0.005113705,"teacher_disagreement_score":0.007942573,"about_ca_system_score_codex":0.00096379203,"about_ca_system_score_gemma":0.0012534622,"threshold_uncertainty_score":0.042004824},"labels":[],"label_agreement":null},{"id":"W2013616647","doi":"10.1111/1467-9876.00281","title":"Generalized Local Influence with Applications to Fish Stock Cohort Analysis","year":2002,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Parametric statistics; Differentiable function; Econometrics; Computer science; Nonparametric statistics; Focus (optics); Parametric model; Data mining; Statistics; Mathematics","score_opus":0.006653418008416818,"score_gpt":0.2253918736231483,"score_spread":0.2187384556147315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013616647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059222717,0.0006763346,0.93692386,0.00030168073,0.00003791298,0.000091341484,0.00012183529,0.00036226277,0.0022620447],"genre_scores_gemma":[0.85044867,0.00056530035,0.14657962,0.00017502336,0.0002020766,0.00021563357,0.00018895828,0.00021653579,0.0014081573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99355316,0.0041588796,0.00026387902,0.0005930612,0.0012270046,0.00020397235],"domain_scores_gemma":[0.8632976,0.12067961,0.0055566607,0.0053780116,0.0040230267,0.0010651638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01217306,0.00067601586,0.0012163613,0.0037256833,0.0007924425,0.0013614214,0.0012972449,0.0009837914,0.002225548],"category_scores_gemma":[0.089256175,0.00037495376,0.0021756464,0.0019622487,0.002809091,0.0015534149,0.0025368568,0.0017950245,0.00019610184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021077623,0.00007062672,0.042190865,0.000275069,0.0007154295,0.0012441505,0.0013481969,0.44118878,0.002642374,0.38533002,0.0024168047,0.12236692],"study_design_scores_gemma":[0.000020142561,0.00010939228,0.006372115,0.00004696525,0.00007759849,0.0002512935,0.000109341265,0.774115,0.00096180785,0.21564674,0.0022383984,0.000051214687],"about_ca_topic_score_codex":0.004628002,"about_ca_topic_score_gemma":0.0036313357,"teacher_disagreement_score":0.01217306,"about_ca_system_score_codex":0.0012272852,"about_ca_system_score_gemma":0.0010442402,"threshold_uncertainty_score":0.06437802},"labels":[],"label_agreement":null},{"id":"W2014033463","doi":"10.1111/1467-9876.00247","title":"Analysis of Repeated Failures or Durations, with Application to Shunt Failures for Patients with Paediatric Hydrocephalus","year":2001,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Cerebrospinal fluid and hydrocephalus","field":"Neuroscience","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Observational study; Hydrocephalus; Econometrics; Multiplicative function; Population; Statistics; Medicine; Mathematics; Surgery","score_opus":0.00711708579269055,"score_gpt":0.23702160413823464,"score_spread":0.2299045183455441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014033463","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7825609,0.026524575,0.18097061,0.0046602935,0.00037650153,0.00028859242,0.00273074,0.00017324224,0.0017145253],"genre_scores_gemma":[0.9756092,0.0018257265,0.020754669,0.00019038498,0.0002795312,0.00025524598,0.00051419024,0.000023860925,0.0005470873],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9796071,0.015356055,0.0016956504,0.001420589,0.0016094067,0.00031112076],"domain_scores_gemma":[0.706587,0.25652537,0.023936095,0.009565,0.0019478407,0.0014386376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03851975,0.0005044463,0.0017535265,0.0018706449,0.00027011835,0.001019196,0.001911197,0.0012880082,0.0036959946],"category_scores_gemma":[0.1736573,0.0002831657,0.0032018258,0.002243187,0.0009716696,0.0011779554,0.0013318222,0.0018475613,0.00015424537],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003924936,0.000234535,0.74617755,0.0036558218,0.008843719,0.0015942479,0.0013556193,0.049202293,0.0011373514,0.017973086,0.0028955115,0.16300529],"study_design_scores_gemma":[0.0003914049,0.0065147365,0.68437684,0.0012256235,0.0053182025,0.0038009675,0.002162472,0.19174811,0.0027637163,0.089379326,0.012035346,0.00028331642],"about_ca_topic_score_codex":0.001728644,"about_ca_topic_score_gemma":0.001655226,"teacher_disagreement_score":0.03851975,"about_ca_system_score_codex":0.00047722226,"about_ca_system_score_gemma":0.0010887001,"threshold_uncertainty_score":0.20371437},"labels":[],"label_agreement":null},{"id":"W2015365216","doi":"10.1111/1467-9876.00416","title":"Tracing Studies and Analysis of the Effect of Loss to Follow-Up on Mortality Estimation from Patient Registry Data","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Medicine; Estimation; Intensive care medicine; Emergency medicine; Medical emergency; Pediatrics","score_opus":0.03274079636972735,"score_gpt":0.341907157579271,"score_spread":0.30916636120954366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015365216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38929653,0.009441004,0.5817849,0.0034194093,0.0007266989,0.0026993984,0.0044446136,0.00074001413,0.007447396],"genre_scores_gemma":[0.8685394,0.0014075564,0.12192294,0.000809664,0.0003179476,0.003767612,0.001449234,0.00018647642,0.0015991718],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.31740597,0.6491804,0.015030067,0.0061490242,0.011118347,0.0011161655],"domain_scores_gemma":[0.04583957,0.8920697,0.029818403,0.028485652,0.0034380374,0.00034856886],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.40499574,0.0008467573,0.0018171967,0.0055773673,0.0010341913,0.0031490505,0.0035341913,0.002061556,0.0037093987],"category_scores_gemma":[0.6958646,0.0009464884,0.0050121173,0.0077510276,0.0028179944,0.0026834016,0.0041905832,0.003014988,0.00044375684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050246036,0.00020186073,0.7615019,0.0029878372,0.015270354,0.0013940862,0.0037921746,0.029128928,0.0008287553,0.032690246,0.0049160062,0.1422633],"study_design_scores_gemma":[0.0012666477,0.006084892,0.6082166,0.007104583,0.02146947,0.0029509284,0.0026328743,0.24785586,0.012743884,0.052339762,0.036737897,0.000596753],"about_ca_topic_score_codex":0.004260781,"about_ca_topic_score_gemma":0.0021437542,"teacher_disagreement_score":0.59500426,"about_ca_system_score_codex":0.0015093237,"about_ca_system_score_gemma":0.0018234148,"threshold_uncertainty_score":0.7337462},"labels":[],"label_agreement":null},{"id":"W2018608434","doi":"10.1111/rssc.12096","title":"A Bayesian Approach for The Analysis of Triadic Data In Cognitive Social Structures","year":2015,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Markov chain Monte Carlo; Inference; Bayesian probability; Machine learning; Bayesian inference; Artificial intelligence; Bayesian network; Software; Markov chain; Data mining; Programming language","score_opus":0.041729477642001384,"score_gpt":0.3268941209096974,"score_spread":0.28516464326769597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018608434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004181659,0.00011210984,0.99490684,0.0001930031,0.000013456767,0.000031396958,0.00011963943,0.00009050507,0.00035138577],"genre_scores_gemma":[0.21641944,0.0004117497,0.7802159,0.00023063476,0.00016008163,0.0006923396,0.00063091674,0.00020112236,0.001037809],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9834831,0.012469425,0.0005774795,0.001491036,0.0016729751,0.0003059658],"domain_scores_gemma":[0.9274574,0.060232867,0.0028383886,0.0055114925,0.0030519173,0.0009079746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02296025,0.00060113653,0.0016392228,0.003972836,0.0014757027,0.0031649934,0.0025533668,0.001493768,0.0045618354],"category_scores_gemma":[0.094831176,0.0010034642,0.0017364622,0.0031234005,0.002467721,0.0040529845,0.0034610399,0.0032220904,0.0007962685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027117526,0.00017929848,0.007909632,0.00036545197,0.0006873253,0.00036617267,0.0014352726,0.23933773,0.0030057817,0.5854631,0.005620466,0.15535855],"study_design_scores_gemma":[0.000018166982,0.000030132906,0.0016385564,0.000047426696,0.000030295014,0.00009585382,0.000081762075,0.5827279,0.00026934038,0.41253614,0.002478305,0.00004614718],"about_ca_topic_score_codex":0.0059188576,"about_ca_topic_score_gemma":0.005702237,"teacher_disagreement_score":0.02296025,"about_ca_system_score_codex":0.0012877353,"about_ca_system_score_gemma":0.0019815103,"threshold_uncertainty_score":0.12142682},"labels":[],"label_agreement":null},{"id":"W2047356869","doi":"10.1111/j.1467-9876.2008.00630.x","title":"Analysis of Interval-Censored Data from Clustered Multistate Processes: Application to Joint Damage in Psoriatic Arthritis","year":2008,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Institute for Clinical Evaluative Sciences; Lunenfeld-Tanenbaum Research Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Multiplicative function; Estimator; Random effects model; Psoriatic arthritis; Statistics; Mathematics; Interval (graph theory); Econometrics; Computer science; Statistical physics; Algorithm; Arthritis; Medicine; Internal medicine; Combinatorics; Physics; Mathematical analysis","score_opus":0.07089477090997676,"score_gpt":0.33222018494010347,"score_spread":0.2613254140301267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047356869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093827024,0.00027147075,0.9050308,0.00029121892,0.000019527428,0.00004189649,0.00011791143,0.000094614574,0.00030548396],"genre_scores_gemma":[0.854446,0.0003600061,0.14343789,0.00009241441,0.00005872604,0.00016684098,0.00031494698,0.000040543793,0.0010825343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835134,0.00093826134,0.00008414354,0.00023956211,0.00029576584,0.00009101463],"domain_scores_gemma":[0.98705566,0.010314562,0.0012451393,0.00070841506,0.0004655702,0.0002105664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066398764,0.00028870496,0.001051913,0.0007803322,0.00029256535,0.0007949951,0.0014512624,0.00072082056,0.0010761105],"category_scores_gemma":[0.015738957,0.0003623582,0.0011878291,0.0008471442,0.00078324054,0.00066080695,0.0011410036,0.0015194641,0.00011206267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017719126,0.00012339427,0.01276693,0.00016383076,0.00030551286,0.00034366903,0.000185991,0.85797447,0.0031347037,0.08433334,0.00058289,0.039908048],"study_design_scores_gemma":[0.0000088555635,0.000028782464,0.0015384136,0.000009596338,0.000016699878,0.000037976955,0.000013662474,0.97286123,0.00028716677,0.024951499,0.00023225596,0.000013780311],"about_ca_topic_score_codex":0.0033489303,"about_ca_topic_score_gemma":0.0025999558,"teacher_disagreement_score":0.0066398764,"about_ca_system_score_codex":0.0006346887,"about_ca_system_score_gemma":0.0009811266,"threshold_uncertainty_score":0.03511548},"labels":[],"label_agreement":null},{"id":"W2054487197","doi":"10.1111/j.1467-9876.2005.00523_1.x","title":"Corrigendum: Designing Fractional Factorial Split-Plot Experiments with Few Whole-Plot Factors","year":2005,"lang":"en","type":"erratum","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mistake; Plot (graphics); Table (database); Fractional factorial design; Mathematics; Statistics; Split plot; Factorial; Factorial experiment; Combinatorics; Arithmetic; Computer science; Mathematical analysis; Data mining; Law","score_opus":0.09048864572351657,"score_gpt":0.37133249024216963,"score_spread":0.2808438445186531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054487197","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023614338,0.0051582735,0.21507074,0.037090965,0.69239676,0.0011098522,0.014261448,0.015464649,0.017085904],"genre_scores_gemma":[0.05681771,0.013688017,0.4518958,0.053281765,0.061944652,0.0054778084,0.024608389,0.02008484,0.31220105],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9913476,0.0033031036,0.00097009155,0.0010032178,0.0031095282,0.00026643064],"domain_scores_gemma":[0.93301713,0.026550984,0.0017767451,0.008365101,0.02933365,0.00095646584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007923337,0.0036516658,0.0031983578,0.0030078043,0.0017967375,0.00196997,0.0041210437,0.0027500365,0.1082317],"category_scores_gemma":[0.1167543,0.0019483949,0.0017975852,0.0037404168,0.0016779191,0.0022490143,0.0013888467,0.0035230552,0.04334075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012227951,0.00003842565,0.0001369216,0.0005243919,0.000030588013,0.00016871514,0.00004161807,0.0004731241,0.0006545475,0.0014313555,0.9518409,0.044537105],"study_design_scores_gemma":[0.00029645712,0.00048051318,0.0036339283,0.00058351824,0.0003328481,0.0011632402,0.00013231377,0.009004607,0.008284423,0.013227535,0.96266025,0.00020040228],"about_ca_topic_score_codex":0.007849354,"about_ca_topic_score_gemma":0.015962064,"teacher_disagreement_score":0.1082317,"about_ca_system_score_codex":0.0029566123,"about_ca_system_score_gemma":0.0027876613,"threshold_uncertainty_score":0.3620711},"labels":[],"label_agreement":null},{"id":"W2054562144","doi":"10.1111/1467-9876.00234","title":"Monitoring Processes with Data Censored Owing to Competing Risks by Using Exponentially Weighted Moving Average Control Charts","year":2001,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Censoring (clinical trials); Control chart; Statistics; Chart; Computer science; Moving average; Control limits; Econometrics; Reliability engineering; Process (computing); Mathematics; Engineering","score_opus":0.0823865255387916,"score_gpt":0.36569762840328013,"score_spread":0.2833111028644885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054562144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019890951,0.00047827774,0.97863996,0.00013970082,0.00004889437,0.000049305818,0.000075007585,0.000340058,0.0003377613],"genre_scores_gemma":[0.72709244,0.00091561774,0.26996478,0.00011019686,0.00019944941,0.00039057713,0.00044021884,0.00008403747,0.0008025912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9905033,0.0058726715,0.00060104305,0.0011131148,0.0016196187,0.00029029002],"domain_scores_gemma":[0.9116322,0.06683969,0.009889186,0.0047053033,0.0061705145,0.0007629777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02588477,0.0013751345,0.001555467,0.002566277,0.0004937052,0.0021719849,0.0018626951,0.0012007185,0.0010406263],"category_scores_gemma":[0.059717298,0.00041643396,0.0013214011,0.0021574467,0.0015299042,0.0020563144,0.0014684295,0.0021159027,0.00015227488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061756483,0.0002200845,0.01819399,0.0004001912,0.0005852914,0.00042854686,0.00034920077,0.6848725,0.003912007,0.13737684,0.0020502622,0.1509936],"study_design_scores_gemma":[0.000022239436,0.00011276521,0.0010992644,0.000026287686,0.00005782483,0.000032470372,0.000011676164,0.9774079,0.001687217,0.018790565,0.000711491,0.00004019709],"about_ca_topic_score_codex":0.0035434782,"about_ca_topic_score_gemma":0.0016073396,"teacher_disagreement_score":0.02588477,"about_ca_system_score_codex":0.00089010294,"about_ca_system_score_gemma":0.0011033271,"threshold_uncertainty_score":0.13689333},"labels":[],"label_agreement":null},{"id":"W2064019275","doi":"10.1111/j.1467-9876.2004.00423.x","title":"Period Analysis of Variable Stars by Robust Smoothing","year":2004,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Outlier; Smoothing spline; Smoothing; Nonparametric statistics; Spline (mechanical); Mathematics; Nonparametric regression; Context (archaeology); Statistics; Cross-validation; Regression; Computer science; Engineering; Spline interpolation; Geography","score_opus":0.030912241870509304,"score_gpt":0.31779095780063005,"score_spread":0.28687871593012076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064019275","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077518545,0.00050111185,0.91937053,0.00011126717,0.00005203759,0.000028856912,0.0002535458,0.00079277094,0.0013713995],"genre_scores_gemma":[0.75947744,0.00037430046,0.2363602,0.00006479843,0.00012804847,0.00007848586,0.0011567317,0.00052865135,0.001831345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99890006,0.00042331452,0.00006257994,0.00025818992,0.0002662298,0.000089497946],"domain_scores_gemma":[0.9935981,0.0028367923,0.001165333,0.0013599809,0.00089078496,0.00014894661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003996687,0.00038416169,0.00069495494,0.0028088894,0.00039958072,0.000908862,0.00093826925,0.00058372313,0.0012953609],"category_scores_gemma":[0.016607922,0.0002886791,0.0010413254,0.001893989,0.0005034301,0.0008508218,0.0008610282,0.0008745768,0.00045466534],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048103175,0.0000670966,0.041188076,0.00034701123,0.00043685292,0.0002805503,0.00036589665,0.44249254,0.017536411,0.050269492,0.0052499,0.44128516],"study_design_scores_gemma":[0.000018456078,0.00006838783,0.026361145,0.000034625256,0.000059567516,0.00015826456,0.000048579543,0.92903477,0.0038071352,0.03519386,0.005149494,0.000065725544],"about_ca_topic_score_codex":0.0022747503,"about_ca_topic_score_gemma":0.0013811925,"teacher_disagreement_score":0.003996687,"about_ca_system_score_codex":0.00047007797,"about_ca_system_score_gemma":0.0005473715,"threshold_uncertainty_score":0.02113676},"labels":[],"label_agreement":null},{"id":"W2064500779","doi":"10.1111/1467-9876.00399","title":"Analysing State Dependences in Emotional Experiences by Dynamic Count Data Models","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Mental Health Research Topics","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Extraversion and introversion; Neuroticism; Psychology; Personality; Big Five personality traits; Anxiety; Multilevel model; Social psychology; Developmental psychology; Clinical psychology; Statistics; Mathematics","score_opus":0.0498641194567644,"score_gpt":0.3775892232328066,"score_spread":0.3277251037760422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064500779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3405606,0.00044525598,0.6549014,0.00090557337,0.00006356989,0.00012416528,0.0013123456,0.0002624785,0.0014246573],"genre_scores_gemma":[0.9516488,0.00021512098,0.045180295,0.0000650282,0.000066987115,0.00026764197,0.0011081151,0.00004718556,0.0014008734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947625,0.0031258955,0.00028563722,0.00095392455,0.0005447316,0.00032730514],"domain_scores_gemma":[0.9295037,0.058845427,0.0051972326,0.004278084,0.0014995526,0.0006760602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01035537,0.0005359838,0.0014386025,0.0018822037,0.000581747,0.0030788353,0.0018696738,0.0009925219,0.0038096916],"category_scores_gemma":[0.04914217,0.0007713535,0.0018684195,0.0023529183,0.001215649,0.0028199486,0.0027147029,0.0025350726,0.00034258125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084421184,0.0004795985,0.2898432,0.00043615815,0.0026301132,0.0008258365,0.0021082545,0.41717854,0.0033127822,0.16342135,0.0023220144,0.11659791],"study_design_scores_gemma":[0.000018076555,0.00011723146,0.022564182,0.000042454696,0.00012406471,0.000087023865,0.00027627364,0.917613,0.000353594,0.057805125,0.0009432916,0.000055723587],"about_ca_topic_score_codex":0.00639571,"about_ca_topic_score_gemma":0.0044478984,"teacher_disagreement_score":0.01035537,"about_ca_system_score_codex":0.0009996983,"about_ca_system_score_gemma":0.0006820217,"threshold_uncertainty_score":0.054765105},"labels":[],"label_agreement":null},{"id":"W2067583296","doi":"10.1111/1467-9876.00270","title":"Missing Time-Dependent Covariates in Human Immunodeficiency Virus Dynamic Models","year":2002,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institutes of Health; National Institute of Allergy and Infectious Diseases; ACT Government","keywords":"Covariate; Imputation (statistics); Missing data; Statistics; Gibbs sampling; Human immunodeficiency virus (HIV); Mathematics; Econometrics; Computer science; Immunology; Medicine","score_opus":0.012288026519160864,"score_gpt":0.24768427756436567,"score_spread":0.2353962510452048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067583296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04538076,0.0016992816,0.9494154,0.0015775596,0.00017367683,0.00010792637,0.00080070057,0.00033412818,0.00051057857],"genre_scores_gemma":[0.76677155,0.001946505,0.22450723,0.0006293221,0.00041230684,0.00064016436,0.002029162,0.00016130638,0.0029024323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9788207,0.017654141,0.00059037976,0.0015258334,0.0009442009,0.00046466244],"domain_scores_gemma":[0.8370638,0.14918911,0.0060834116,0.0047273356,0.0021824825,0.00075379066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038527634,0.0011145041,0.0032190536,0.0013052772,0.00081836706,0.0019090489,0.0032927964,0.0028754305,0.0025982996],"category_scores_gemma":[0.120317936,0.0010340824,0.0019456522,0.0027417268,0.0016788058,0.0027289235,0.0020221802,0.0035983215,0.0004213344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005432881,0.00011808222,0.02373791,0.0005171967,0.0008971916,0.0007209265,0.00031544376,0.853801,0.0004613637,0.06736701,0.002588101,0.0489325],"study_design_scores_gemma":[0.00009275114,0.00015294558,0.0029413605,0.00011457706,0.00015278973,0.0002227685,0.000055269316,0.8808495,0.00035355685,0.11268226,0.0023192684,0.00006300101],"about_ca_topic_score_codex":0.006743393,"about_ca_topic_score_gemma":0.005257744,"teacher_disagreement_score":0.038527634,"about_ca_system_score_codex":0.0011899185,"about_ca_system_score_gemma":0.0021363045,"threshold_uncertainty_score":0.20375603},"labels":[],"label_agreement":null},{"id":"W2095980813","doi":"10.1111/j.1467-9876.2011.01004.x","title":"Spatial Modelling of Lupus Incidence Over 40 Years with Changes in Census Areas","year":2011,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; Cancer Care Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Cancer Care Ontario","keywords":"Laplace's method; Inference; Bayesian probability; Systemic lupus erythematosus; Bayesian inference; Statistics; Population; Grid; Computer science; Geography; Cartography; Econometrics; Mathematics; Medicine; Artificial intelligence; Environmental health","score_opus":0.03203816137096858,"score_gpt":0.2623367982455295,"score_spread":0.23029863687456095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095980813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93771774,0.0005442302,0.053350326,0.001113643,0.00004594345,0.000106021165,0.003901804,0.00034992475,0.0028704228],"genre_scores_gemma":[0.99318236,0.00016993248,0.0035983818,0.000019411053,0.000011410299,0.000040041865,0.0007883223,0.00001674565,0.0021733493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990274,0.0004195441,0.000052217016,0.00023948806,0.00010698655,0.0001545122],"domain_scores_gemma":[0.9954972,0.0024632527,0.0009543315,0.00039377497,0.00044358172,0.00024776586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002066593,0.0003956761,0.00058644277,0.001198343,0.00037987003,0.0013579044,0.0017366341,0.00089211727,0.0021645527],"category_scores_gemma":[0.0090050455,0.0004618039,0.0010666436,0.0018084877,0.0011864281,0.0007203182,0.0010149572,0.0008140097,0.00034378012],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008518754,0.000026182754,0.045564875,0.000023245662,0.000061378065,0.00014619033,0.00019414896,0.94376796,0.00024088971,0.0062761707,0.00047912076,0.0031346339],"study_design_scores_gemma":[0.000018831966,0.000032897136,0.015832048,0.000009903411,0.000028984452,0.00005799033,0.00010462827,0.98032963,0.00009080514,0.0027669321,0.0007093888,0.000017985978],"about_ca_topic_score_codex":0.32608584,"about_ca_topic_score_gemma":0.17195886,"teacher_disagreement_score":0.32608584,"about_ca_system_score_codex":0.0038651144,"about_ca_system_score_gemma":0.0017643546,"threshold_uncertainty_score":0.64837563},"labels":[],"label_agreement":null},{"id":"W2110742637","doi":"10.1046/j.1467-9876.2003.05029.x","title":"Designing Fractional Factorial Split-Plot Experiments with Few Whole-Plot Factors","year":2004,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Fractional factorial design; Split plot; Restricted randomization; Plot (graphics); Factorial; Factorial experiment; Mathematics; Design of experiments; Main effect; Statistics; Table (database); Computer science; Algorithm; Arithmetic; Data mining; Randomization","score_opus":0.07178356171609879,"score_gpt":0.369274802522208,"score_spread":0.2974912408061092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110742637","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032664675,0.00009850964,0.96316373,0.00010946668,0.00008098322,0.001875776,0.0001543086,0.00054995064,0.0013025614],"genre_scores_gemma":[0.1088617,0.00008220976,0.8850255,0.00011318761,0.000026509746,0.0050858827,0.00015567912,0.00011090716,0.0005384313],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9719153,0.02160563,0.0011031611,0.002131885,0.00275346,0.0004905922],"domain_scores_gemma":[0.9174437,0.061225098,0.005410724,0.009637566,0.005421416,0.00086147507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032405905,0.0010605247,0.0017578447,0.001148529,0.0008056106,0.0012868874,0.0015362215,0.0009828049,0.0051567894],"category_scores_gemma":[0.08540112,0.0008435465,0.0012223073,0.00088821654,0.001624919,0.001509282,0.0011419386,0.001495797,0.0011571462],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010388012,0.0025255769,0.012622119,0.004433426,0.0008118822,0.00035883568,0.0018117287,0.15420158,0.0990926,0.1597924,0.0084699765,0.54549193],"study_design_scores_gemma":[0.004172916,0.017217657,0.012398235,0.0007321381,0.00057239464,0.00033772446,0.0006469555,0.48072323,0.11974079,0.32369593,0.039355468,0.000406534],"about_ca_topic_score_codex":0.00027325677,"about_ca_topic_score_gemma":0.00048287874,"teacher_disagreement_score":0.032405905,"about_ca_system_score_codex":0.0010221184,"about_ca_system_score_gemma":0.0016608111,"threshold_uncertainty_score":0.17138082},"labels":[],"label_agreement":null},{"id":"W2121525579","doi":"10.1111/rssc.12091","title":"Analysis of Multivariate Failure Times in the Presence of Selection Bias with Application to Breast Cancer","year":2014,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"BRCA gene mutations in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre hospitalier universitaire de Québec; Université Laval","funders":"Ministero dello Sviluppo Economico","keywords":"Statistic; Test statistic; Breast cancer; Statistics; Copula (linguistics); Multivariate statistics; Selection (genetic algorithm); Genotype; Statistical hypothesis testing; Mutation; Biology; Econometrics; Oncology; Mathematics; Cancer; Genetics; Computer science; Medicine; Gene","score_opus":0.005596548127275249,"score_gpt":0.255088550800663,"score_spread":0.24949200267338775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121525579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32892162,0.0008885115,0.66753757,0.00058175944,0.00011214343,0.00019876107,0.00035048317,0.0006188343,0.0007903655],"genre_scores_gemma":[0.93169993,0.00030333645,0.06544324,0.00007106808,0.00016783955,0.00033848616,0.00043989063,0.00014681449,0.0013894954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9830058,0.013485196,0.0005111425,0.0013315403,0.0010297513,0.000636426],"domain_scores_gemma":[0.6991322,0.27424282,0.012543555,0.008565709,0.003540531,0.001975217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05431491,0.0010323195,0.0022584037,0.0027865826,0.00081254856,0.0013730995,0.0021263994,0.0012546009,0.004435502],"category_scores_gemma":[0.123934984,0.0005319415,0.002182703,0.0021931317,0.0021227673,0.0014482851,0.0020811786,0.0024147239,0.0003743184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023382888,0.0004352725,0.19748244,0.0004944321,0.0017544435,0.001626839,0.0013018192,0.5659046,0.0023040557,0.06688451,0.003287382,0.15618579],"study_design_scores_gemma":[0.000043887263,0.00027261255,0.011422498,0.000024593133,0.00009363357,0.00015371676,0.00008546713,0.97290015,0.00037679923,0.014074468,0.00051538163,0.000036702804],"about_ca_topic_score_codex":0.003980423,"about_ca_topic_score_gemma":0.00189813,"teacher_disagreement_score":0.05431491,"about_ca_system_score_codex":0.00096969836,"about_ca_system_score_gemma":0.0017937125,"threshold_uncertainty_score":0.28724813},"labels":[],"label_agreement":null},{"id":"W2123482702","doi":"10.1111/1467-9876.00179","title":"Designing and Integrating Composite Networks for Monitoring Multivariate Gaussian Pollution Fields","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"BC Cancer Agency; Statistics Canada; University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Research Councils UK","keywords":"Pollution; Multivariate statistics; Environmental science; Gaussian; Multivariate normal distribution; Computer science; Maximization; Pollutant; Hyperparameter; Air pollution; Statistics; Mathematics; Mathematical optimization; Machine learning; Ecology","score_opus":0.0254967679320331,"score_gpt":0.3160598567177564,"score_spread":0.2905630887857233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123482702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04352883,0.0000892709,0.9553599,0.000091294954,0.000008091043,0.000047465932,0.000057839818,0.00027874566,0.0005386143],"genre_scores_gemma":[0.4695794,0.00016219346,0.5284497,0.000077620854,0.00004876028,0.00025615445,0.0002773249,0.00007196962,0.0010769003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975804,0.0011581932,0.00008626901,0.00060843385,0.00040805564,0.00015864817],"domain_scores_gemma":[0.9920632,0.0051786597,0.001103031,0.0005163793,0.00087442074,0.0002641891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071423934,0.00073578383,0.0011789427,0.0017542121,0.0006687119,0.0014533928,0.0019285016,0.0011279339,0.0009681217],"category_scores_gemma":[0.014926697,0.0010623555,0.0009610641,0.0014100141,0.0013392097,0.0022385994,0.0028314914,0.0011258089,0.00018579586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025136003,0.00009910976,0.0071680453,0.000054753506,0.00009141711,0.00007006066,0.00015498548,0.9160232,0.0034724062,0.013130049,0.00032925935,0.05915542],"study_design_scores_gemma":[0.000007735001,0.00003140869,0.00069986423,0.000003885436,0.0000148636345,0.000009332185,0.000011527646,0.99155915,0.0008309374,0.0065807924,0.00024320863,0.0000073087563],"about_ca_topic_score_codex":0.008046822,"about_ca_topic_score_gemma":0.012278767,"teacher_disagreement_score":0.008046822,"about_ca_system_score_codex":0.0019093453,"about_ca_system_score_gemma":0.001226,"threshold_uncertainty_score":0.037773073},"labels":[],"label_agreement":null},{"id":"W2127403744","doi":"10.1111/1467-9876.00182","title":"Bayesian Sample Size Determination for Estimating Binomial Parameters from Data Subject to Misclassification","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Montreal General Hospital; McGill University","funders":"","keywords":"Sample size determination; Statistics; Sample (material); Bayesian probability; Degree (music); Mathematics; Binomial (polynomial); Binomial distribution; Negative binomial distribution; Econometrics; Computer science; Poisson distribution","score_opus":0.05710207231426012,"score_gpt":0.3451959507327966,"score_spread":0.28809387841853645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127403744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014480783,0.00036965334,0.98354924,0.00053362915,0.000033323955,0.00019922158,0.0000660827,0.00009252797,0.00067551195],"genre_scores_gemma":[0.3212196,0.0006712553,0.67453164,0.00046741017,0.000181364,0.0015812318,0.00042930007,0.00012950492,0.00078871136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93352944,0.055373177,0.002017765,0.0034573146,0.0050095906,0.0006127482],"domain_scores_gemma":[0.44794574,0.5225195,0.011482048,0.010435569,0.006648188,0.0009690152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12573767,0.0009214055,0.0030650876,0.0031127674,0.0011558083,0.0024796862,0.0037700296,0.0028832091,0.0021788357],"category_scores_gemma":[0.47040942,0.0014654276,0.001330175,0.0018579625,0.003937011,0.003553144,0.0033862623,0.00451145,0.00038503757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089195697,0.0002322715,0.030146843,0.0012418656,0.000829546,0.0008731597,0.0018521751,0.37330323,0.0033286589,0.36531368,0.0040033623,0.21798328],"study_design_scores_gemma":[0.00014618777,0.00013478691,0.0036917091,0.00033836855,0.000076128395,0.00024740092,0.00014056863,0.65443546,0.0015532969,0.33711085,0.0020591416,0.00006609459],"about_ca_topic_score_codex":0.0033780697,"about_ca_topic_score_gemma":0.0024473008,"teacher_disagreement_score":0.12573767,"about_ca_system_score_codex":0.0018680709,"about_ca_system_score_gemma":0.0022108636,"threshold_uncertainty_score":0.6649723},"labels":[],"label_agreement":null},{"id":"W2130299120","doi":"10.1111/1467-9876.00180","title":"Using Orientation Statistics to Investigate Variations in Human Kinematics","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Mechanics and Biomechanics Studies","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Orientation (vector space); Kinematics; Torso; Rotation (mathematics); Tangent; Mathematics; Geodesy; Geology; Statistics; Geometry; Anatomy; Physics","score_opus":0.017581026633065287,"score_gpt":0.2517752198711958,"score_spread":0.23419419323813048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130299120","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8263396,0.0003054428,0.16886866,0.00012136098,0.000058651698,0.000050931732,0.00065921596,0.0003855553,0.0032105995],"genre_scores_gemma":[0.9880804,0.00006097437,0.011243405,0.000014173389,0.000023048944,0.000028075707,0.00036562158,0.000046027184,0.00013828378],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968714,0.0018624823,0.00018283827,0.00044014864,0.0005160883,0.00012695648],"domain_scores_gemma":[0.98155415,0.014350528,0.0016337272,0.0014367925,0.00081360876,0.00021122715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032080715,0.00039475676,0.00053828314,0.002192926,0.0002307319,0.00086749316,0.0001893166,0.0002775333,0.0011676847],"category_scores_gemma":[0.023779789,0.00014593727,0.00036476512,0.002090781,0.00085395254,0.0005252793,0.000470764,0.00037621713,0.0003072406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018941974,0.000205327,0.46978876,0.00023131727,0.0008305488,0.0005339712,0.0016592713,0.07344549,0.057589997,0.008999308,0.002515455,0.38230643],"study_design_scores_gemma":[0.000052053743,0.0012403358,0.8143714,0.00004704996,0.00014610129,0.0011733513,0.0012022774,0.14587149,0.014021953,0.017717179,0.0039920546,0.00016476674],"about_ca_topic_score_codex":0.0012219946,"about_ca_topic_score_gemma":0.0007100638,"teacher_disagreement_score":0.0032080715,"about_ca_system_score_codex":0.00020322729,"about_ca_system_score_gemma":0.00029732028,"threshold_uncertainty_score":0.016966105},"labels":[],"label_agreement":null},{"id":"W2134429012","doi":"10.1111/j.1467-9876.2012.01041.x","title":"Estimating the Optimal Dynamic Antipsychotic Treatment Regime: Evidence from the Sequential Multiple-Assignment Randomized Clinical Antipsychotic Trials of Intervention and Effectiveness Schizophrenia Study","year":2012,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Antipsychotic; Schizophrenia (object-oriented programming); Perphenazine; Randomized controlled trial; Psychiatry; Intervention (counseling); Marginal structural model; Clinical trial; Psychology; Medicine; Confidence interval; Internal medicine","score_opus":0.14126718593338197,"score_gpt":0.45643209670626445,"score_spread":0.31516491077288245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134429012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55795777,0.19527917,0.18297958,0.024922458,0.0051442906,0.009934636,0.0057728468,0.0010439093,0.016965313],"genre_scores_gemma":[0.941518,0.01537342,0.03513997,0.0027183786,0.0008065611,0.0020101052,0.0015229624,0.00007813612,0.0008325796],"study_design_codex":"randomized_trial","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.83172387,0.15830919,0.0041858926,0.002339387,0.0029234393,0.00051826733],"domain_scores_gemma":[0.6627822,0.30538192,0.017901557,0.009634984,0.0027966737,0.0015026585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.096714124,0.0014067802,0.0055406033,0.0011579716,0.0004975157,0.0015406574,0.001767384,0.002069297,0.007809147],"category_scores_gemma":[0.2568448,0.00084997143,0.0058636065,0.0016084702,0.0021144082,0.0024387492,0.0018144128,0.004877169,0.00042199466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.507051,0.0027651638,0.013391038,0.020454396,0.09067459,0.00020478055,0.0006484386,0.031697907,0.00057264883,0.028271558,0.009678896,0.29458952],"study_design_scores_gemma":[0.51853275,0.04682411,0.023624694,0.0068889987,0.1465496,0.00049512554,0.00033452048,0.09688801,0.001576736,0.14028864,0.017709251,0.0002876277],"about_ca_topic_score_codex":0.0021487654,"about_ca_topic_score_gemma":0.0013836062,"teacher_disagreement_score":0.096714124,"about_ca_system_score_codex":0.0010314451,"about_ca_system_score_gemma":0.0026226493,"threshold_uncertainty_score":0.51147926},"labels":[],"label_agreement":null},{"id":"W2136037152","doi":"10.1111/rssc.12036","title":"Statistical Inference and Computational Efficiency for Spatial Infectious Disease Models with Plantation Data","year":2013,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cancer Care Ontario; University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Inference; Computer science; Gibbs sampling; Monte Carlo method; Markov chain; Bayesian inference; Algorithm; Data mining; Machine learning; Statistics; Artificial intelligence; Mathematics; Bayesian probability","score_opus":0.035369886723969755,"score_gpt":0.3173897390464121,"score_spread":0.28201985232244237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136037152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09283314,0.0004753169,0.90228826,0.0014714636,0.000029090666,0.00005532503,0.0002506454,0.00058185,0.0020149942],"genre_scores_gemma":[0.6052289,0.00039739237,0.39220485,0.00017797554,0.00006763867,0.00019748947,0.000483162,0.00019249655,0.0010502225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99434483,0.0044658603,0.00020461433,0.00045065949,0.00040550673,0.0001286174],"domain_scores_gemma":[0.868965,0.124554746,0.0016626404,0.0033624268,0.0010920606,0.00036313967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017163545,0.00042882096,0.0010876972,0.0015507385,0.0006075722,0.0016902568,0.0018970718,0.0010100699,0.0023088239],"category_scores_gemma":[0.088878796,0.00066382164,0.0009884674,0.0015538002,0.0014434638,0.0027279116,0.0017415788,0.0018962612,0.00039872705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015693739,0.000067199006,0.008956861,0.00011965908,0.00014359732,0.00015474761,0.00017872971,0.8874654,0.0007538945,0.0684714,0.0008016567,0.032729853],"study_design_scores_gemma":[0.0000095909045,0.0000074398504,0.0003727765,0.000009174688,0.0000063048105,0.000020722737,0.000021156642,0.9697231,0.00013946084,0.029503448,0.00018136913,0.000005399728],"about_ca_topic_score_codex":0.0120871905,"about_ca_topic_score_gemma":0.010672647,"teacher_disagreement_score":0.017163545,"about_ca_system_score_codex":0.0013622353,"about_ca_system_score_gemma":0.0021117437,"threshold_uncertainty_score":0.0907706},"labels":[],"label_agreement":null},{"id":"W2146689060","doi":"10.1111/rssc.12002","title":"Calendarization with Interpolating Splines and State Space Models","year":2013,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; State space; Spline (mechanical); Mathematics; Interpolation (computer graphics); Space (punctuation); Computer science; State-space representation; Algorithm; State (computer science); Applied mathematics; Statistics; Artificial intelligence","score_opus":0.0037193536458099458,"score_gpt":0.18455556761660458,"score_spread":0.18083621397079463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146689060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014452591,0.00015209765,0.9842216,0.00020178355,0.00002920336,0.000015933852,0.00012120523,0.00019871241,0.00060689833],"genre_scores_gemma":[0.5866827,0.0006045545,0.40701276,0.00010129022,0.00013332568,0.00018510439,0.00090520084,0.00024848824,0.004126596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971213,0.0018155245,0.00013549585,0.00036622383,0.00038885835,0.00017264004],"domain_scores_gemma":[0.9878682,0.008972545,0.001078275,0.0011488647,0.00068935804,0.00024278257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008525902,0.0005664123,0.0011099909,0.0017604387,0.00060933485,0.0014469933,0.0016621114,0.001224674,0.0028006888],"category_scores_gemma":[0.027025647,0.00053331244,0.0013844373,0.0030394008,0.0013473629,0.0022044259,0.0019828745,0.002447862,0.00036224336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081959806,0.00003364288,0.002526427,0.00004827731,0.000039748735,0.00004986883,0.000118808,0.82393605,0.00031988404,0.13520078,0.00071564823,0.036928967],"study_design_scores_gemma":[0.00000436675,0.0000074976833,0.00023933547,0.00000724822,0.0000029090659,0.00000516328,0.000008626292,0.960842,0.00013715008,0.038213894,0.0005237715,0.000008020233],"about_ca_topic_score_codex":0.009398381,"about_ca_topic_score_gemma":0.005024594,"teacher_disagreement_score":0.009398381,"about_ca_system_score_codex":0.0011562092,"about_ca_system_score_gemma":0.0015609019,"threshold_uncertainty_score":0.04508984},"labels":[],"label_agreement":null},{"id":"W2147628913","doi":"10.1111/rssc.12038","title":"A Generalized Quasi-Likelihood Scoring Approach for Simultaneously Testing the Genetic Association of Multiple Traits","year":2013,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Type I and type II errors; Trait; Flexibility (engineering); Statistics; Genetic association; Statistical hypothesis testing; Multiple comparisons problem; Association (psychology); Computer science; Binary data; Binary number; Biology; Mathematics; Genetics; Psychology","score_opus":0.011457810622266579,"score_gpt":0.22019657983859126,"score_spread":0.20873876921632467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147628913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032478159,0.00007143315,0.9960355,0.0001229311,0.000021412478,0.00007753167,0.00005097537,0.00013971054,0.00023268165],"genre_scores_gemma":[0.13236566,0.00013312139,0.86333454,0.00027148682,0.00012781005,0.0007313247,0.00049441896,0.00019207799,0.0023495753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97009575,0.024558023,0.0007187548,0.0016486049,0.002580723,0.00039820236],"domain_scores_gemma":[0.93167007,0.05563633,0.0027349568,0.00502906,0.0040047416,0.000924845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03547952,0.0013372125,0.0022539739,0.0019082958,0.0009054336,0.001669394,0.0060955053,0.0021187903,0.005191343],"category_scores_gemma":[0.06884608,0.0010314186,0.0019425091,0.0025290777,0.002383502,0.0018296647,0.0030621225,0.0028529426,0.0012424955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006955111,0.00040812616,0.016691944,0.0005732391,0.0012772792,0.0009855414,0.00045433128,0.4670664,0.0048196726,0.15354326,0.0069466596,0.34653813],"study_design_scores_gemma":[0.000068938134,0.00014410865,0.0013652896,0.00001978257,0.000044303943,0.00012635303,0.00002712521,0.94574875,0.000323888,0.0510138,0.0010837034,0.000033965123],"about_ca_topic_score_codex":0.005248898,"about_ca_topic_score_gemma":0.006456328,"teacher_disagreement_score":0.03547952,"about_ca_system_score_codex":0.0011426252,"about_ca_system_score_gemma":0.0034115028,"threshold_uncertainty_score":0.18763584},"labels":[],"label_agreement":null},{"id":"W2161543129","doi":"10.1111/rssc.12062","title":"Combining the Bayesian Processor of Output with Bayesian Model Averaging for Reliable Ensemble Forecasting","year":2014,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"GDG Environnement; Université Laval","funders":"Mitacs; Hydro-Québec; Manitoba Hydro; Université Laval","keywords":"Bayesian probability; Ensemble forecasting; Computer science; Bayesian inference; Bayesian average; Ensemble learning; Bayesian statistics; Statistical ensemble; Set (abstract data type); Machine learning; Artificial intelligence; Statistics; Monte Carlo method; Mathematics; Canonical ensemble","score_opus":0.020105675772043162,"score_gpt":0.21450506677626682,"score_spread":0.19439939100422365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161543129","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028814038,0.00008066178,0.9694992,0.00011533415,0.000017705444,0.000026077812,0.000049970105,0.000372614,0.0010244369],"genre_scores_gemma":[0.67338705,0.00018141474,0.32481876,0.00009993756,0.000101166144,0.00012638124,0.00032024304,0.00018462235,0.0007804158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865603,0.00062352925,0.000058092413,0.00014076564,0.00042483266,0.000096616524],"domain_scores_gemma":[0.99408925,0.0039082593,0.00035695278,0.0006368796,0.00091905816,0.000089705056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003648477,0.00055064046,0.0009730037,0.00079776044,0.00037910492,0.000990464,0.00092404603,0.00047484433,0.0010319651],"category_scores_gemma":[0.019395836,0.00046518337,0.0004516866,0.0009847583,0.00050452276,0.0018100239,0.0011798419,0.0014185882,0.00030389306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045900368,0.000033424676,0.0020899829,0.000018541694,0.000071236915,0.000025617886,0.00005370116,0.902099,0.0010902117,0.008398088,0.00080362253,0.08527059],"study_design_scores_gemma":[0.00000253009,0.0000061844,0.00020763364,0.0000014381799,0.000005025872,0.0000026248401,0.0000020645186,0.99588203,0.00024698925,0.0035059922,0.00013316575,0.000004426983],"about_ca_topic_score_codex":0.019223409,"about_ca_topic_score_gemma":0.016796762,"teacher_disagreement_score":0.019223409,"about_ca_system_score_codex":0.0006484761,"about_ca_system_score_gemma":0.0019435652,"threshold_uncertainty_score":0.038223088},"labels":[],"label_agreement":null},{"id":"W2162726810","doi":"10.1111/rssc.12109","title":"Estimating Controlled Direct Effects of Restrictive Feeding Practices in the ‘Early Dieting in Girls’ Study","year":2015,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Child Health and Human Development; National Institute on Drug Abuse; National Institutes of Health","keywords":"Confounding; Propensity score matching; Logistic regression; Mediation; Dieting; Weighting; Causal inference; Econometrics; Inverse probability weighting; Statistics; Curse of dimensionality; Psychology; Regression; Mathematics; Medicine; Obesity; Weight loss; Internal medicine","score_opus":0.10455509817073741,"score_gpt":0.43515233408012943,"score_spread":0.33059723590939205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162726810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9781104,0.0015832651,0.016410723,0.0004552686,0.000110617504,0.00030687932,0.002275807,0.000029811335,0.0007172257],"genre_scores_gemma":[0.9866758,0.00040661555,0.009853193,0.00018055677,0.000050302315,0.000683548,0.0014040637,0.000014818473,0.00073100044],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9819239,0.01451111,0.0007599247,0.0017998072,0.0006316716,0.0003736742],"domain_scores_gemma":[0.95482635,0.03030221,0.0068232496,0.0066841138,0.00070118246,0.00066286925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02510031,0.00080913614,0.0012667358,0.00070530106,0.0008204971,0.0012231533,0.001766184,0.0013484232,0.0032991404],"category_scores_gemma":[0.053327817,0.0007464265,0.0033395104,0.0013998803,0.001334467,0.0007588953,0.0028103977,0.002098738,0.00035780095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002197906,0.00039545854,0.96473944,0.00031348036,0.0074060042,0.00047970176,0.0011872812,0.0013229772,0.0006420234,0.0030999754,0.000760433,0.017455341],"study_design_scores_gemma":[0.00089763425,0.002007738,0.9634791,0.00028516052,0.006867639,0.0006758261,0.0013392523,0.009206351,0.0015516764,0.0067898817,0.006825511,0.000074264244],"about_ca_topic_score_codex":0.018230194,"about_ca_topic_score_gemma":0.010941404,"teacher_disagreement_score":0.02510031,"about_ca_system_score_codex":0.00065525435,"about_ca_system_score_gemma":0.0013099625,"threshold_uncertainty_score":0.13274467},"labels":[],"label_agreement":null},{"id":"W2270764716","doi":"10.1111/rssc.12147","title":"A Regional Compound Poisson Process for Hurricane and Tropical Storm Damage","year":2016,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Tropical and Extratropical Cyclones Research","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Storm; Poisson process; Environmental science; Meteorology; Climatology; Bayesian probability; Poisson distribution; Econometrics; Geography; Computer science; Geology; Statistics; Mathematics; Artificial intelligence","score_opus":0.019126847274987636,"score_gpt":0.2582382627821843,"score_spread":0.23911141550719667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270764716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69053876,0.00051605265,0.30008867,0.0014228335,0.00008488134,0.0001476543,0.0019011894,0.00034596413,0.0049540466],"genre_scores_gemma":[0.9863543,0.00021133502,0.009277868,0.00006364004,0.000049813105,0.00005879323,0.00065981987,0.00003072853,0.0032936293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912935,0.000297201,0.00004000652,0.00025967936,0.00016397574,0.00010970901],"domain_scores_gemma":[0.9919376,0.004734,0.0018164009,0.00059233996,0.00066919293,0.00025047082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050368686,0.00046166877,0.0006733557,0.00073155883,0.00029246663,0.0010140728,0.001413661,0.0009283808,0.0047844816],"category_scores_gemma":[0.011559386,0.00028957083,0.001192902,0.00062800397,0.0009145871,0.0011846579,0.0007787692,0.0013730053,0.0004854428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000278886,0.00006807277,0.047372203,0.0000617309,0.00013245485,0.0006980217,0.00030443355,0.79746056,0.0018714191,0.13494796,0.0029812963,0.013822973],"study_design_scores_gemma":[0.000016168868,0.000048058322,0.011580351,0.000013521104,0.00003304286,0.00015589634,0.000072199844,0.9684757,0.00032586142,0.01863123,0.0006127974,0.00003520938],"about_ca_topic_score_codex":0.017214391,"about_ca_topic_score_gemma":0.008923145,"teacher_disagreement_score":0.017214391,"about_ca_system_score_codex":0.0012386423,"about_ca_system_score_gemma":0.0005559471,"threshold_uncertainty_score":0.034228384},"labels":[],"label_agreement":null},{"id":"W2272266927","doi":"10.1111/rssc.12432","title":"Cluster Analysis of Microbiome Data by Using Mixtures of Dirichlet–Multinomial Regression Models","year":2020,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Covariate; Microbiome; Multinomial distribution; Regression; Mixture model; Multinomial logistic regression; Regression analysis; Dirichlet distribution; Biology; Biota; Probabilistic logic; Statistics; Ecology; Mathematics; Econometrics; Bioinformatics","score_opus":0.02440155035215219,"score_gpt":0.28621045248769006,"score_spread":0.26180890213553787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272266927","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012310862,0.00026844873,0.9861223,0.00023233275,0.00004216134,0.000077660174,0.0002397071,0.0005313838,0.00017510391],"genre_scores_gemma":[0.26550347,0.00051978976,0.7274218,0.00021752906,0.00027400086,0.0006891545,0.002498958,0.00053363235,0.0023416574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9852829,0.0103346985,0.0006486288,0.0022754786,0.00096745987,0.00049086957],"domain_scores_gemma":[0.9728424,0.020598887,0.001945973,0.002664246,0.0014245104,0.0005239471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018829465,0.0012223433,0.0024961282,0.003711034,0.0013363168,0.002954965,0.0036872677,0.0019678606,0.0025556777],"category_scores_gemma":[0.043295946,0.0011589266,0.0063868864,0.0031461034,0.0018639314,0.0024334255,0.0034055503,0.0037327574,0.0012450751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007125745,0.0001819903,0.011899609,0.00032965423,0.001050576,0.00026975814,0.0012326693,0.750882,0.0043996824,0.08895904,0.0040985504,0.13598388],"study_design_scores_gemma":[0.000011772117,0.00002033378,0.0005813677,0.000016742566,0.000018665787,0.000031542633,0.00004023241,0.97247803,0.00035487712,0.025534833,0.00088056276,0.00003118368],"about_ca_topic_score_codex":0.011030854,"about_ca_topic_score_gemma":0.008743611,"teacher_disagreement_score":0.018829465,"about_ca_system_score_codex":0.0019542454,"about_ca_system_score_gemma":0.0018088992,"threshold_uncertainty_score":0.09958094},"labels":[],"label_agreement":null},{"id":"W2509334837","doi":"10.1111/rssc.12227","title":"Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":195,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Canada; Dalhousie University; University of British Columbia","funders":"Centre for Doctoral Training in Statistical Applied Mathematics, University of Bath; Engineering and Physical Sciences Research Council; World Health Organization","keywords":"Air quality index; Environmental science; Air pollution; Particulates; Aerosol; Pollution; Meteorology; Population; Satellite; Grid; Estimation; Geography; Environmental health; Engineering","score_opus":0.07737935096800294,"score_gpt":0.3640514137665068,"score_spread":0.2866720627985039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509334837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019712247,0.0003066598,0.97517055,0.0010259032,0.00004397508,0.00010687846,0.0010339819,0.00049092644,0.0021088875],"genre_scores_gemma":[0.63755554,0.0007762572,0.34578508,0.00068083254,0.00021433669,0.0011143978,0.0046564704,0.00033936233,0.008877818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963825,0.0019036721,0.00019066999,0.00069387193,0.0005812969,0.0002480349],"domain_scores_gemma":[0.9932689,0.004996598,0.00057804753,0.00027081644,0.0007528657,0.00013271872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00657479,0.0011001382,0.001757956,0.0015864772,0.0008842929,0.0021624877,0.0031295784,0.0017284087,0.0032881955],"category_scores_gemma":[0.013518763,0.0009941624,0.0023750903,0.0024916336,0.0012371218,0.0019510968,0.0027635598,0.0023604669,0.00062783994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003127017,0.000029743904,0.0029608177,0.00003077944,0.00008691685,0.000070803144,0.000071154114,0.95295054,0.00016344305,0.031618293,0.0010921025,0.010894198],"study_design_scores_gemma":[0.0000057510865,0.0000067091164,0.00028610742,0.000004552635,0.000012793618,0.000005891929,0.000007771438,0.99013966,0.000026937827,0.009195046,0.00030271552,0.0000059812273],"about_ca_topic_score_codex":0.098774076,"about_ca_topic_score_gemma":0.053710613,"teacher_disagreement_score":0.098774076,"about_ca_system_score_codex":0.00313351,"about_ca_system_score_gemma":0.0034151736,"threshold_uncertainty_score":0.19639832},"labels":[],"label_agreement":null},{"id":"W2547243051","doi":"10.1111/rssc.12192","title":"Estimation of the Population Size by Using the One-Inflated Positive Poisson Model","year":2016,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Census and Population Estimation","field":"Mathematics","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Poisson distribution; Statistics; Estimator; Population size; Econometrics; Estimation; Population; Poisson regression; Count data; Inflation (cosmology); Mathematics; Demography; Economics; Physics","score_opus":0.023873171181706062,"score_gpt":0.29641662025839227,"score_spread":0.2725434490766862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547243051","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017064726,0.00013795772,0.98136926,0.00026879707,0.00004518663,0.00004390151,0.0001005744,0.00010730386,0.000862272],"genre_scores_gemma":[0.5947114,0.0007749049,0.39700896,0.0004226252,0.00030614957,0.000463623,0.0011024979,0.00018113376,0.005028718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957891,0.002604552,0.00018604496,0.00061191915,0.00060384837,0.00020460224],"domain_scores_gemma":[0.9831322,0.012306203,0.001353927,0.0018717935,0.0011095924,0.00022632671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008453668,0.00050366117,0.001146598,0.001645777,0.0004882246,0.0012355355,0.0036512027,0.0011643531,0.0027018972],"category_scores_gemma":[0.042607922,0.0004509391,0.0018884983,0.0018117763,0.0016143143,0.0023603186,0.0021914886,0.0022199727,0.0008126879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011959289,0.00009330049,0.023871617,0.00032375436,0.00032679344,0.0007841176,0.00092996424,0.37333357,0.0041167205,0.45089737,0.005429303,0.13977396],"study_design_scores_gemma":[0.0000104834635,0.00004432867,0.0028903913,0.00004598265,0.000036191737,0.0003146269,0.00006283183,0.83533627,0.00058813917,0.15834954,0.002280825,0.00004041209],"about_ca_topic_score_codex":0.0037544614,"about_ca_topic_score_gemma":0.0023692101,"teacher_disagreement_score":0.008453668,"about_ca_system_score_codex":0.00074982794,"about_ca_system_score_gemma":0.0010829928,"threshold_uncertainty_score":0.044707775},"labels":[],"label_agreement":null},{"id":"W2581041162","doi":"10.1111/rssc.12212","title":"A Spatiotemporal Model for Extreme Precipitation Simulated by a Climate Model, With an Application to Assessing Changes in Return Levels Over North America","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Hydro-Québec","keywords":"Precipitation; Flooding (psychology); Flood myth; Climatology; Environmental science; Climate model; Climate change; Return period; Statistical model; Grid; Meteorology; Computer science; Geography; Geology; Artificial intelligence","score_opus":0.022239877659702954,"score_gpt":0.2813790212386147,"score_spread":0.2591391435789117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581041162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54909587,0.00023539818,0.43892804,0.0015677192,0.00006504599,0.00007762559,0.003748724,0.0008354589,0.005446099],"genre_scores_gemma":[0.9717459,0.00020347958,0.02502671,0.0000559452,0.00002880595,0.0001266546,0.0009246351,0.00006849093,0.0018194482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978167,0.00008184123,0.00002180613,0.00006797343,0.000028038881,0.000018654458],"domain_scores_gemma":[0.9989046,0.0006815877,0.0001761387,0.000060705755,0.00012700788,0.000049880455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009546376,0.00035661773,0.00034054506,0.00041408098,0.0003708839,0.00080214127,0.0009812887,0.00074215664,0.0015259135],"category_scores_gemma":[0.0036828911,0.00035663715,0.0005940594,0.00083058304,0.00048591112,0.0007315264,0.0006083345,0.00068329234,0.00017323218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014660264,0.000012886253,0.0030657211,0.0000049877613,0.000014557473,0.000028001989,0.000016984535,0.99120164,0.00019898951,0.0042427317,0.00018456251,0.0010142359],"study_design_scores_gemma":[0.0000028084098,0.0000039181386,0.0003958165,7.029768e-7,0.0000027015426,0.0000040227774,0.000003423026,0.9988336,0.000020226802,0.0006457301,0.00008536137,0.000001728064],"about_ca_topic_score_codex":0.052437846,"about_ca_topic_score_gemma":0.03087375,"teacher_disagreement_score":0.94756216,"about_ca_system_score_codex":0.001072369,"about_ca_system_score_gemma":0.0010775205,"threshold_uncertainty_score":0.10426521},"labels":[],"label_agreement":null},{"id":"W2590528016","doi":"10.1111/rssc.12216","title":"Simultaneously Modelling Clustered Marginal Counts and Multinomial Proportions with Zero Inflation with Application to Analysis of Osteoporotic Fractures Data","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Saint Vincent University; University of New Brunswick","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Multinomial distribution; National Health and Nutrition Examination Survey; Randomness; Osteoporotic fracture; Statistics; Osteoporosis; Econometrics; Medicine; Demography; Mathematics; Environmental health; Population; Internal medicine; Bone mineral","score_opus":0.022251251016820617,"score_gpt":0.3243899365837531,"score_spread":0.30213868556693246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590528016","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0722902,0.00046683353,0.9245947,0.0009010376,0.000106476626,0.00020348404,0.00051470345,0.0003527126,0.0005697536],"genre_scores_gemma":[0.6091054,0.000500644,0.38336614,0.00041368388,0.00026475743,0.0012547339,0.0014163088,0.00021397976,0.0034644315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9517521,0.040014207,0.0013287764,0.004068646,0.0016956355,0.0011405802],"domain_scores_gemma":[0.8113766,0.16352428,0.010303902,0.00955794,0.0038644224,0.0013729151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052066945,0.0014384942,0.0035129718,0.0024779614,0.0013419772,0.0032611503,0.005767269,0.0029561918,0.0028399657],"category_scores_gemma":[0.14935704,0.0017319806,0.004668467,0.0034998215,0.0037574535,0.0030391437,0.005022944,0.0051233205,0.000599713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076090026,0.00030405982,0.070618995,0.00039535697,0.0014550539,0.0009319981,0.0023898943,0.68122685,0.000739067,0.17228436,0.0021846883,0.06670871],"study_design_scores_gemma":[0.00005317218,0.000144416,0.0048581,0.000050809544,0.0000944511,0.000103393984,0.00020639677,0.9054716,0.00018798234,0.0874854,0.0012736679,0.000070616035],"about_ca_topic_score_codex":0.017961364,"about_ca_topic_score_gemma":0.012892303,"teacher_disagreement_score":0.052066945,"about_ca_system_score_codex":0.0027222822,"about_ca_system_score_gemma":0.0031592068,"threshold_uncertainty_score":0.27535957},"labels":[],"label_agreement":null},{"id":"W2594515121","doi":"10.1111/rssc.12232","title":"Contextual Ranking by Passive Safety of Generational Classes of Light Vehicles","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche; Association Nationale de la Recherche et de la Technologie; European Commission; U.S. Department of Transportation","keywords":"SAFER; Context (archaeology); Ranking (information retrieval); Set (abstract data type); Computer science; Class (philosophy); Oracle; Function (biology); Service (business); Artificial intelligence; Computer security; Geography; Business; Marketing","score_opus":0.006161745458789785,"score_gpt":0.2112531824301632,"score_spread":0.20509143697137341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594515121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7356722,0.0015805328,0.25179678,0.00086964,0.00012267733,0.00015822286,0.0040597366,0.0017521902,0.0039880085],"genre_scores_gemma":[0.94066143,0.00009719734,0.05294062,0.00007129606,0.00006122342,0.000058232014,0.005117841,0.000098512784,0.00089374103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9972357,0.0013186835,0.0001579371,0.00068508316,0.00037639836,0.00022622044],"domain_scores_gemma":[0.9912422,0.0055409973,0.00058158714,0.0013210145,0.00094823004,0.00036590136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038034695,0.0007295183,0.0009491651,0.0020000564,0.00056869135,0.0015193267,0.0011499504,0.0007142802,0.0022496413],"category_scores_gemma":[0.0147517305,0.00019652188,0.001005446,0.001345649,0.0005305657,0.00095702946,0.0010823734,0.0010394447,0.00049501075],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010753536,0.0004266557,0.16700006,0.00032390593,0.00056119025,0.00015435058,0.0003118287,0.40342712,0.0032036202,0.010186269,0.014520331,0.39880928],"study_design_scores_gemma":[0.000052891395,0.00022635699,0.02787347,0.000039419705,0.000095754585,0.00009007551,0.00015800608,0.95028645,0.0029689353,0.014950473,0.0032092663,0.00004899428],"about_ca_topic_score_codex":0.0065787057,"about_ca_topic_score_gemma":0.009398084,"teacher_disagreement_score":0.0065787057,"about_ca_system_score_codex":0.0009569419,"about_ca_system_score_gemma":0.0009400169,"threshold_uncertainty_score":0.020114958},"labels":[],"label_agreement":null},{"id":"W2626895545","doi":"10.1111/rssc.12226","title":"Pattern–Mixture Models with Incomplete Informative Cluster Size: Application to a Repeated Pregnancy Study","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute; National Institutes of Health","keywords":"Mixture model; Parity (physics); Cluster (spacecraft); Pregnancy; Latent variable; Statistics; Statistical model; Gestational age; Sample size determination; Mathematics; Computer science; Biology","score_opus":0.011031979333497833,"score_gpt":0.25961999533179203,"score_spread":0.2485880159982942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626895545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05509041,0.0008602083,0.9412244,0.0011902791,0.0000815317,0.00020122503,0.00027939037,0.00021136687,0.0008611666],"genre_scores_gemma":[0.5152876,0.0010376866,0.47560248,0.00044650483,0.00023773086,0.0012065578,0.0007261041,0.00020910162,0.005246242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927003,0.0060691256,0.00015914427,0.0005820903,0.00030867814,0.00018068872],"domain_scores_gemma":[0.89275074,0.09690044,0.0028488368,0.004281536,0.0022236693,0.0009948036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031248719,0.0010611907,0.0022210788,0.0015278159,0.0011910712,0.0018694059,0.004426741,0.0034808514,0.0030978895],"category_scores_gemma":[0.08606691,0.0012827489,0.002716813,0.002333184,0.0023275798,0.0017317152,0.0031824354,0.004610095,0.0004063654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008780091,0.00032810573,0.027315594,0.000342624,0.0010758177,0.0011109458,0.0018456299,0.6037454,0.0011107408,0.28938556,0.0042066537,0.06865481],"study_design_scores_gemma":[0.00009459906,0.000060009537,0.0017872127,0.000037023343,0.00007966275,0.00009529401,0.00008001437,0.92031455,0.00013503281,0.076165326,0.0011154427,0.000035770954],"about_ca_topic_score_codex":0.025774978,"about_ca_topic_score_gemma":0.01989686,"teacher_disagreement_score":0.031248719,"about_ca_system_score_codex":0.0019665174,"about_ca_system_score_gemma":0.0019797836,"threshold_uncertainty_score":0.16526097},"labels":[],"label_agreement":null},{"id":"W2790780849","doi":"10.1111/rssc.12262","title":"Using Artificial Censoring to Improve Extreme Tail Quantile Estimates","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Censoring (clinical trials); Quantile; Copula (linguistics); Percentile; Inference; Statistics; Parametric statistics; Computer science; Range (aeronautics); Mathematics; Econometrics; Artificial intelligence; Engineering","score_opus":0.12965437206746003,"score_gpt":0.3661660513134425,"score_spread":0.23651167924598246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790780849","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007991767,0.000264825,0.99050623,0.00015986802,0.000044850272,0.000015688993,0.00006028671,0.0005125123,0.00044396715],"genre_scores_gemma":[0.39381725,0.00048606703,0.6026743,0.00036551335,0.00023980737,0.00010219756,0.0006886659,0.00045494427,0.0011712412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9910381,0.00641403,0.00042964358,0.0006901143,0.0011808818,0.0002472316],"domain_scores_gemma":[0.93841225,0.043402415,0.0035117501,0.00924476,0.0045139655,0.0009148542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018378962,0.0008607698,0.0012945376,0.0019952406,0.00045517593,0.001680057,0.0019431399,0.0013510964,0.0032031212],"category_scores_gemma":[0.0742879,0.00049061066,0.0011617496,0.0021103304,0.0014880523,0.002043495,0.0029768166,0.0033857264,0.0010949173],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079365546,0.00023018409,0.02435106,0.0004523771,0.0005024176,0.00049049465,0.00045469304,0.54156816,0.00960986,0.09175366,0.0072635324,0.32252988],"study_design_scores_gemma":[0.00003989059,0.000109154644,0.0031955764,0.0000825888,0.000042503096,0.00015253582,0.000029945248,0.93811643,0.004093483,0.049578514,0.0044970377,0.00006231977],"about_ca_topic_score_codex":0.001804766,"about_ca_topic_score_gemma":0.0015249867,"teacher_disagreement_score":0.018378962,"about_ca_system_score_codex":0.0005670505,"about_ca_system_score_gemma":0.0010875528,"threshold_uncertainty_score":0.09719837},"labels":[],"label_agreement":null},{"id":"W2799996621","doi":"10.1111/rssc.12286","title":"Methods for Preferential Sampling in Geostatistics","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Geostatistics; Sampling (signal processing); Statistics; Function (biology); Importance sampling; Set (abstract data type); Kriging; Statistical physics; Mathematics; Computer science; Econometrics; Algorithm; Applied mathematics; Spatial variability; Physics","score_opus":0.02343015867472422,"score_gpt":0.32091634864106583,"score_spread":0.29748618996634163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799996621","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054524466,0.00009661555,0.998965,0.00008361817,0.000018714441,0.000023076787,0.000028458007,0.00007464696,0.00016463173],"genre_scores_gemma":[0.066182904,0.0003928938,0.93099135,0.00023088114,0.00018440695,0.00061164703,0.0002642717,0.00018661912,0.000955145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97157323,0.022307536,0.0011222882,0.0016845291,0.0029316633,0.00038069434],"domain_scores_gemma":[0.8526457,0.12706903,0.0033708443,0.01072151,0.00536018,0.0008328639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034570936,0.0010683887,0.0014850209,0.0028459704,0.00086967647,0.0022153459,0.002749027,0.0014734581,0.0047661164],"category_scores_gemma":[0.13246709,0.0010072072,0.0021209416,0.0031502903,0.0038933835,0.0027309475,0.004620277,0.0046112747,0.0013876898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015520303,0.00008263896,0.003928914,0.0005320113,0.00031023368,0.00025200518,0.00041441427,0.19457848,0.0014517664,0.6737436,0.0052005,0.1193502],"study_design_scores_gemma":[0.000046104204,0.000041871954,0.00047901095,0.00010582967,0.000028775348,0.00014096426,0.000035483677,0.5146211,0.0008471911,0.47833788,0.0052839704,0.000031921987],"about_ca_topic_score_codex":0.0025360496,"about_ca_topic_score_gemma":0.0034193732,"teacher_disagreement_score":0.034570936,"about_ca_system_score_codex":0.0015140481,"about_ca_system_score_gemma":0.003024664,"threshold_uncertainty_score":0.18283075},"labels":[],"label_agreement":null},{"id":"W2802826956","doi":"10.1111/rssc.12279","title":"A Non-Linear Model for Censored and Mismeasured Time Varying Covariates in Survival Models, with Applications in Human Immunodeficiency Virus and Acquired Immune Deficiency Syndrome Studies","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"College of Staten Island, City University of New York; Research Foundation of The City University of New York; City University of New York; National Science Foundation","keywords":"Covariate; Proportional hazards model; Statistics; Linear model; Econometrics; Survival analysis; Mathematics","score_opus":0.04311923441272773,"score_gpt":0.3284523403737827,"score_spread":0.28533310596105493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802826956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004449737,0.00079760293,0.99329925,0.0007926347,0.000057953817,0.000038375154,0.000113616305,0.00007953344,0.00037116255],"genre_scores_gemma":[0.34896916,0.0038456067,0.62691814,0.0011155348,0.0006797049,0.0012721948,0.0010752057,0.00030763546,0.015816785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98717415,0.010540279,0.000298076,0.0010841619,0.0006649332,0.00023845874],"domain_scores_gemma":[0.90059584,0.09212575,0.0034496407,0.0018533303,0.0014748808,0.0005005287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030466596,0.001355678,0.001865149,0.0018037603,0.00086828956,0.0018990962,0.003346547,0.0028345922,0.0045517324],"category_scores_gemma":[0.06571619,0.0010191196,0.0025143535,0.0023570086,0.0026253997,0.0031963077,0.0024614458,0.004655508,0.0007783867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013897188,0.000085319196,0.0052597583,0.00038191854,0.00027761087,0.00048030994,0.00044341836,0.4171264,0.00050431764,0.53606606,0.003246314,0.035989657],"study_design_scores_gemma":[0.000029105842,0.00008029096,0.00085537403,0.00008793631,0.00006473439,0.00013576008,0.00004916417,0.7571178,0.0001987749,0.23768926,0.0036474473,0.000044294386],"about_ca_topic_score_codex":0.007318929,"about_ca_topic_score_gemma":0.0076288446,"teacher_disagreement_score":0.030466596,"about_ca_system_score_codex":0.0019001097,"about_ca_system_score_gemma":0.0019645744,"threshold_uncertainty_score":0.1611247},"labels":[],"label_agreement":null},{"id":"W2886011145","doi":"10.1111/rssc.12305","title":"Joint Modelling of a Binary and a Continuous Outcome Measured at Two Cycles to Determine the Optimal Dose","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Cancer Research; Institut National Du Cancer","keywords":"Toxicity; Estimator; Probit model; Outcome (game theory); Probit; Maximum tolerated dose; Statistics; Computer science; Mathematics; Medicine; Internal medicine","score_opus":0.38681761127283804,"score_gpt":0.4428733709425216,"score_spread":0.05605575966968357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886011145","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111702405,0.00043832842,0.8850734,0.0004746466,0.00006113721,0.0008276927,0.00034503953,0.00030352827,0.0007738273],"genre_scores_gemma":[0.7809986,0.00019929594,0.21438043,0.0002312083,0.000048207497,0.0020113084,0.00042708748,0.00006495126,0.0016389666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9800396,0.015363956,0.00076449645,0.0016947198,0.0016574212,0.00047969664],"domain_scores_gemma":[0.96020067,0.030539645,0.0040043187,0.0035786591,0.001206063,0.00047054177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02703726,0.0008588588,0.0024589205,0.0008177138,0.000249296,0.0013069161,0.0016160113,0.0016408329,0.003311011],"category_scores_gemma":[0.04622441,0.00073571655,0.0020437867,0.0008542967,0.0011920143,0.0010119834,0.0013884758,0.0024001468,0.00052202726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0074821822,0.0009510169,0.033304036,0.00067032024,0.0008013309,0.00023115531,0.0003043537,0.7534965,0.010472457,0.028208345,0.0016839672,0.16239432],"study_design_scores_gemma":[0.0003912925,0.0019425716,0.006594453,0.0000673713,0.00023951395,0.00012628819,0.000022373484,0.9659095,0.0049223313,0.018173235,0.0015521371,0.000058922506],"about_ca_topic_score_codex":0.0010768059,"about_ca_topic_score_gemma":0.0007871032,"teacher_disagreement_score":0.02703726,"about_ca_system_score_codex":0.0011566966,"about_ca_system_score_gemma":0.0022923443,"threshold_uncertainty_score":0.14298844},"labels":[],"label_agreement":null},{"id":"W2894790280","doi":"10.1111/rssc.12312","title":"Rectangular Latent Markov Models for Time-Specific Clustering, with An Analysis of the Wellbeing of Nations","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Merge (version control); Markov chain; Latent class model; Markov model; Markov process; Cluster analysis; Econometrics; Computer science; Statistics; Mathematics; Information retrieval","score_opus":0.01185984865433809,"score_gpt":0.2459645314030187,"score_spread":0.2341046827486806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894790280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0114772245,0.00072178006,0.98551846,0.0006199413,0.000047691603,0.000044178243,0.00037407427,0.00017245571,0.0010241057],"genre_scores_gemma":[0.5458059,0.0031771841,0.43002823,0.00043050214,0.00047218325,0.0012068838,0.0022869618,0.00039697252,0.016195143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966491,0.0022099605,0.00013132325,0.0005611668,0.00023446605,0.0002140409],"domain_scores_gemma":[0.97903377,0.017048355,0.0015038858,0.0012507177,0.00072754524,0.00043561574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009707646,0.0010382625,0.0017598146,0.0021316693,0.00091631984,0.0020245835,0.0031714132,0.0019239782,0.0057937773],"category_scores_gemma":[0.024809277,0.0010660406,0.0024853426,0.00244257,0.0026462562,0.0032837072,0.0024821148,0.0032316872,0.0011805176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009977732,0.00003787864,0.0020001077,0.00009636942,0.00013287355,0.0001381474,0.0004771736,0.36483675,0.00051251875,0.61656684,0.0019084306,0.013193208],"study_design_scores_gemma":[0.000011396273,0.000020275349,0.00053402747,0.00003102476,0.000031314765,0.000030017023,0.000044602602,0.76285446,0.000092150396,0.23502521,0.00129672,0.00002881142],"about_ca_topic_score_codex":0.014295883,"about_ca_topic_score_gemma":0.012356077,"teacher_disagreement_score":0.014295883,"about_ca_system_score_codex":0.0024507851,"about_ca_system_score_gemma":0.0016822097,"threshold_uncertainty_score":0.051339567},"labels":[],"label_agreement":null},{"id":"W2900101937","doi":"10.1111/rssc.12320","title":"Bayesian Analysis of Functional Magnetic Resonance Imaging Data with Spatially Varying Auto-Regressive Orders","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Functional magnetic resonance imaging; Voxel; Computer science; Bayesian probability; Autoregressive model; Artificial intelligence; Prior probability; Noise (video); Magnetic resonance imaging; Functional data analysis; Pattern recognition (psychology); Machine learning; Mathematics; Statistics; Psychology","score_opus":0.03503791284395981,"score_gpt":0.30485147612959884,"score_spread":0.26981356328563905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900101937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03065312,0.00042538808,0.96727544,0.0005674132,0.000020815154,0.000052865806,0.00018373452,0.0001988915,0.00062248897],"genre_scores_gemma":[0.6708085,0.0010174838,0.32258883,0.0003998944,0.00019569456,0.0004137327,0.0010190007,0.0002180037,0.0033388077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99544996,0.002844954,0.00020280814,0.00066759705,0.0006166425,0.00021813984],"domain_scores_gemma":[0.95829076,0.03560193,0.00232688,0.0017721775,0.0015813039,0.0004269311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015539351,0.00075799366,0.0015248946,0.0016300218,0.0005702217,0.0017892026,0.002195791,0.0020679317,0.001626745],"category_scores_gemma":[0.042527825,0.0009784587,0.0016678253,0.0012903092,0.002013057,0.0021651757,0.0016464086,0.0026594668,0.0003953367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018211397,0.00006189129,0.0032666533,0.00018444336,0.00017410531,0.00020726865,0.00021230047,0.836568,0.0025077513,0.12033487,0.001224418,0.035076126],"study_design_scores_gemma":[0.000015061852,0.000021446995,0.0009421598,0.000018722267,0.000016039867,0.000030224235,0.000010728453,0.9402902,0.00030697664,0.057921004,0.000406007,0.000021476058],"about_ca_topic_score_codex":0.010657578,"about_ca_topic_score_gemma":0.012107462,"teacher_disagreement_score":0.015539351,"about_ca_system_score_codex":0.0019217583,"about_ca_system_score_gemma":0.0016524792,"threshold_uncertainty_score":0.08218092},"labels":[],"label_agreement":null},{"id":"W2906421128","doi":"10.1111/rssc.12334","title":"Landmark Linear Transformation Model for Dynamic Prediction with Application to A Longitudinal Cohort Study of Chronic Disease","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Landmark; Cohort; Transformation (genetics); Longitudinal data; Medicine; Computer science; Physical medicine and rehabilitation; Artificial intelligence; Biology; Internal medicine; Data mining; Genetics","score_opus":0.008516178599761086,"score_gpt":0.27172997897067724,"score_spread":0.2632138003709162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906421128","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015612614,0.0002842925,0.9826444,0.00041210852,0.00004776172,0.00004230675,0.00024107442,0.00042143243,0.00029406784],"genre_scores_gemma":[0.6885627,0.0011220485,0.3000494,0.0003399025,0.0003389757,0.0010653854,0.002042988,0.00034592752,0.006132622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975649,0.0016754138,0.00006228703,0.00039806028,0.00017888087,0.00012042961],"domain_scores_gemma":[0.9866644,0.010467733,0.0010075028,0.000923258,0.0006676365,0.00026950205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008309163,0.000861029,0.0013919862,0.0011570035,0.00054982863,0.0010734833,0.002201576,0.0011404776,0.0031223034],"category_scores_gemma":[0.022129757,0.00044365597,0.0014795759,0.0017470197,0.0011843233,0.0012780238,0.0015624475,0.0027062693,0.0008829954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046461044,0.0001244829,0.016972365,0.00013865264,0.00037911235,0.00041657183,0.00036165753,0.7929175,0.0013595821,0.09019056,0.00509969,0.09157529],"study_design_scores_gemma":[0.000030093157,0.00010357619,0.00086044206,0.000009533349,0.00003463034,0.00005286089,0.000024270172,0.9702804,0.0001716426,0.027237518,0.0011676755,0.00002726945],"about_ca_topic_score_codex":0.008800059,"about_ca_topic_score_gemma":0.0065555144,"teacher_disagreement_score":0.008800059,"about_ca_system_score_codex":0.00083214097,"about_ca_system_score_gemma":0.0015120958,"threshold_uncertainty_score":0.043943644},"labels":[],"label_agreement":null},{"id":"W2916840027","doi":"10.1111/rssc.12340","title":"Estimation of the Von Bertalanffy Growth Model When Ages are Measured With Error","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Estimator; Parametric statistics; Statistics; Observational error; Mathematics; Standard error; Parametric model; Computer science; Algorithm","score_opus":0.00668510075305194,"score_gpt":0.19639189810945026,"score_spread":0.18970679735639834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916840027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21692646,0.00039207283,0.7773034,0.0007023622,0.000050766623,0.00009427353,0.00087831833,0.00030118643,0.0033510975],"genre_scores_gemma":[0.84176946,0.00026022553,0.15185511,0.000089426685,0.000031252282,0.00024010715,0.0014288544,0.0001242795,0.0042012893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972242,0.0016364739,0.0001065745,0.00040475948,0.00042690177,0.00020099957],"domain_scores_gemma":[0.9679528,0.027237207,0.0020096845,0.0012304235,0.0013112338,0.00025859228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014747593,0.0009056972,0.0012772524,0.0012866723,0.000627857,0.0012540723,0.0023045705,0.0015166223,0.0024466526],"category_scores_gemma":[0.04367491,0.0006556217,0.0010515642,0.001730368,0.0012274921,0.001545027,0.0012280482,0.0021646833,0.00052267226],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007722751,0.00002133472,0.012725229,0.000049153154,0.00005172675,0.000077295284,0.000119516866,0.93549544,0.00024890905,0.03532351,0.0007921558,0.015018491],"study_design_scores_gemma":[0.000009453749,0.000016533166,0.0016753288,0.000015637233,0.000008500908,0.000025344025,0.000021288795,0.9777049,0.00017041864,0.019732052,0.0006018247,0.00001878045],"about_ca_topic_score_codex":0.041516803,"about_ca_topic_score_gemma":0.022386927,"teacher_disagreement_score":0.041516803,"about_ca_system_score_codex":0.002165285,"about_ca_system_score_gemma":0.0017176217,"threshold_uncertainty_score":0.08255029},"labels":[],"label_agreement":null},{"id":"W2923565714","doi":"10.1111/rssc.12342","title":"Modelling Extreme Rain Accumulation with an Application to the 2011 Lake Champlain Flood","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"National Oceanic and Atmospheric Administration; Mitacs; Canada Research Chairs","keywords":"Generalized Pareto distribution; Copula (linguistics); Flood myth; Generalized extreme value distribution; Joint probability distribution; Marginal distribution; Precipitation; Extreme value theory; Return period; Distribution (mathematics); Mathematics; Cluster (spacecraft); Environmental science; Statistics; Hydrology (agriculture); Econometrics; Meteorology; Geography; Geology; Random variable; Computer science; Mathematical analysis","score_opus":0.014336798843077302,"score_gpt":0.23168991847011963,"score_spread":0.21735311962704232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923565714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.953205,0.00009011654,0.042373892,0.00047091997,0.000019219828,0.000059971047,0.00070349924,0.00039015815,0.0026872933],"genre_scores_gemma":[0.988362,0.00003453762,0.009687766,0.00002835033,0.000010009964,0.00004225992,0.0002352452,0.00002113015,0.0015787351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986374,0.00004244855,0.000006281652,0.000025514773,0.000022286791,0.000039610874],"domain_scores_gemma":[0.9995278,0.0002866335,0.000048547518,0.000021104688,0.000072247065,0.000043770524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005721832,0.00041074655,0.00045541747,0.0005613937,0.000622243,0.0009341732,0.00082527875,0.00096375856,0.0013319979],"category_scores_gemma":[0.0012560113,0.000263341,0.00045191593,0.00064845855,0.0005944227,0.00032815724,0.00066806475,0.0006006732,0.0000846329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023767945,0.000030203479,0.005086619,0.0000070942788,0.000014706909,0.00008449242,0.000034021734,0.99155515,0.00034183273,0.0009914708,0.00026419133,0.0015665251],"study_design_scores_gemma":[0.0000034288191,0.0000067002284,0.0014775972,9.841342e-7,0.0000016887889,0.0000060521875,0.000014555335,0.99793965,0.00008584638,0.00033451547,0.00012520987,0.0000037699679],"about_ca_topic_score_codex":0.17858936,"about_ca_topic_score_gemma":0.18574813,"teacher_disagreement_score":0.82141066,"about_ca_system_score_codex":0.0017039225,"about_ca_system_score_gemma":0.0013600497,"threshold_uncertainty_score":0.35509968},"labels":[],"label_agreement":null},{"id":"W2953620293","doi":"10.1111/rssc.12415","title":"Causal Mechanism of Extreme River Discharges in the Upper Danube Basin Network","year":2020,"lang":"en","type":"preprint","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich; Centre de Recherches Mathématiques; Fondation HEC; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Extreme value theory; Causality (physics); Quantile; Tributary; Multivariate statistics; Causal inference; Structural basin; Inference; Generalized extreme value distribution; Econometrics; Construct (python library); Environmental science; Geography; Mathematics; Statistics; Computer science; Geology; Cartography; Physics; Geomorphology; Artificial intelligence","score_opus":0.030343065484235352,"score_gpt":0.24814198666522216,"score_spread":0.21779892118098682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953620293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.976556,0.00017423772,0.019741688,0.00086562824,0.000008227264,0.000021655085,0.0004177723,0.000073130395,0.002141617],"genre_scores_gemma":[0.99876153,0.000034264973,0.0008690681,0.000013116912,0.000003705141,0.000007773122,0.00008607792,0.000002538848,0.00022193827],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995577,0.00018094039,0.0000257339,0.00012882889,0.00006326468,0.00004349441],"domain_scores_gemma":[0.9961287,0.0023273136,0.0008236291,0.00025516772,0.00027140687,0.00019366686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027667,0.00012284538,0.00029892413,0.0015074379,0.00058808445,0.00093932834,0.000545671,0.00038270542,0.0026381915],"category_scores_gemma":[0.0070617986,0.00016888029,0.0002930086,0.0010959823,0.000920646,0.00077370775,0.00085467944,0.0005229668,0.000061934865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014100599,0.00007022615,0.74238014,0.00012427784,0.00025932412,0.0011936021,0.0010556048,0.07558366,0.0028483192,0.13642457,0.001976166,0.037943188],"study_design_scores_gemma":[0.000035873058,0.00003930447,0.41425723,0.000066195,0.000093637114,0.00042118988,0.00087942,0.31986964,0.001272451,0.2602777,0.002736928,0.00005052076],"about_ca_topic_score_codex":0.012795944,"about_ca_topic_score_gemma":0.013503243,"teacher_disagreement_score":0.012795944,"about_ca_system_score_codex":0.0010224627,"about_ca_system_score_gemma":0.0006537006,"threshold_uncertainty_score":0.025442958},"labels":[],"label_agreement":null},{"id":"W2963297536","doi":"10.1111/rssc.12373","title":"A Hidden Semi-Markov Model for Characterizing Regime Shifts in Ocean Density Variability","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Climate variability and models","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Climatology; Temperature salinity diagrams; Markov chain; Markov chain Monte Carlo; Bayesian probability; Precipitation; Statistical physics; Environmental science; Geology; Salinity; Meteorology; Statistics; Oceanography; Mathematics; Geography; Physics","score_opus":0.010759950624337516,"score_gpt":0.22763442458770536,"score_spread":0.21687447396336784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963297536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20363313,0.00029974311,0.7915839,0.00085478043,0.00005132771,0.00007483351,0.0010273244,0.00037265907,0.0021022512],"genre_scores_gemma":[0.9649944,0.00021048647,0.03074139,0.00009389911,0.00006317081,0.00021250565,0.00088354986,0.000051414696,0.0027492682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992055,0.00034003495,0.000044970413,0.00017757162,0.00009913406,0.00013278083],"domain_scores_gemma":[0.9904653,0.007716782,0.0008878959,0.00033275384,0.00040274335,0.00019459131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003892269,0.0005965036,0.0010560554,0.0011860919,0.00071063515,0.0011929447,0.0019885132,0.0013244881,0.0029874342],"category_scores_gemma":[0.009489253,0.00059471314,0.0012437606,0.00086620764,0.0015943326,0.0015340392,0.0009549409,0.0017694228,0.00042146217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007194218,0.000027585475,0.0037141263,0.00002358136,0.000049070317,0.00007612571,0.00008287612,0.95422333,0.0004483378,0.037565242,0.00042061182,0.0032971457],"study_design_scores_gemma":[0.0000043405703,0.0000058560636,0.0002721339,0.0000030119747,0.0000043148166,0.0000048544257,0.000004290918,0.9906701,0.000031231077,0.008945238,0.00004985555,0.00000483026],"about_ca_topic_score_codex":0.022901196,"about_ca_topic_score_gemma":0.019582735,"teacher_disagreement_score":0.022901196,"about_ca_system_score_codex":0.0017440966,"about_ca_system_score_gemma":0.0012915055,"threshold_uncertainty_score":0.045535803},"labels":[],"label_agreement":null},{"id":"W2963491578","doi":"10.1111/rssc.12368","title":"Multi-Dimensional Penalized Hazard Model with Continuous Covariates: Applications for Studying Trends and Social Inequalities in Cancer Survival","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Cancer Research; Hospices Civils de Lyon; Institut National Du Cancer; Ministère de l'Enseignement Supérieur et de la Recherche; Agence Nationale de la Recherche","keywords":"Covariate; Mathematics; Estimator; Econometrics; Proportional hazards model; Statistics; Hazard; Regression","score_opus":0.10232678702001716,"score_gpt":0.3651711043667085,"score_spread":0.26284431734669134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963491578","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019186772,0.0002403622,0.97946006,0.00043168713,0.00002964562,0.000030198315,0.0001457088,0.00014495094,0.00033059515],"genre_scores_gemma":[0.5260407,0.00060453,0.46752802,0.00023308107,0.0002083062,0.0005302506,0.00074547465,0.00022571092,0.0038838715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975556,0.0019005835,0.000061988794,0.00021382145,0.00017887495,0.00008918293],"domain_scores_gemma":[0.9836723,0.014030087,0.0008339345,0.0006156558,0.0006051504,0.00024282308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007360566,0.0005395763,0.0009212655,0.0010920266,0.00039962243,0.00085164997,0.001659199,0.00089798303,0.0025788993],"category_scores_gemma":[0.018375477,0.00045871807,0.0014527937,0.0012501017,0.0008187274,0.0008831965,0.0015924892,0.002328695,0.00029669504],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018273863,0.00008914417,0.008468408,0.00013640987,0.00016721003,0.00022473506,0.00015670733,0.8754028,0.00073882355,0.073249854,0.0018048888,0.039378278],"study_design_scores_gemma":[0.000006015726,0.000016642218,0.00042760425,0.000006469893,0.000008380596,0.000016305441,0.000009325258,0.9860167,0.000059180824,0.013022619,0.0004042569,0.000006543237],"about_ca_topic_score_codex":0.0054621,"about_ca_topic_score_gemma":0.0043899952,"teacher_disagreement_score":0.007360566,"about_ca_system_score_codex":0.0007795688,"about_ca_system_score_gemma":0.0011876452,"threshold_uncertainty_score":0.03892684},"labels":[],"label_agreement":null},{"id":"W2964002974","doi":"10.1111/rssc.12169","title":"Estimating Whole-Brain Dynamics by Using Spectral Clustering","year":2016,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health","funders":"Engineering and Physical Sciences Research Council; Alberta Health Services","keywords":"Computer science; Data mining; Cluster analysis; Series (stratigraphy); Node (physics); Spectral clustering; Data set; Artificial intelligence; Set (abstract data type); Time series; Multivariate statistics; Pattern recognition (psychology); Algorithm; Machine learning","score_opus":0.01806090013214348,"score_gpt":0.2576678354307227,"score_spread":0.2396069352985792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964002974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05484559,0.00035913583,0.94310343,0.00016584937,0.000020338583,0.000034451074,0.00020265405,0.00043585346,0.00083257747],"genre_scores_gemma":[0.7498453,0.0004527416,0.24685426,0.00005322779,0.00007144204,0.000089350855,0.0009979551,0.0002156651,0.0014200313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995389,0.00015248808,0.00002361832,0.00016678301,0.000092011855,0.000026228454],"domain_scores_gemma":[0.9982761,0.0009698093,0.00022441104,0.00027140454,0.00020081592,0.000057435773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012949667,0.0006004588,0.0005419127,0.002437339,0.0003596744,0.0008149896,0.0008173367,0.0006265888,0.0012330906],"category_scores_gemma":[0.006003573,0.00035504304,0.0006813217,0.0015345373,0.00056990463,0.0013071047,0.00067436206,0.00074342935,0.0004677115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001212298,0.00007159943,0.0072413315,0.00013464771,0.00034451636,0.00015153828,0.00018953072,0.7959429,0.017218687,0.021982895,0.002188274,0.15441279],"study_design_scores_gemma":[0.000002149694,0.0000091336515,0.0020368302,0.000007103885,0.0000090568965,0.000026402216,0.00002072138,0.97594416,0.00086623547,0.020565197,0.0005004955,0.000012550785],"about_ca_topic_score_codex":0.00440575,"about_ca_topic_score_gemma":0.0043158783,"teacher_disagreement_score":0.00440575,"about_ca_system_score_codex":0.00052343355,"about_ca_system_score_gemma":0.000529425,"threshold_uncertainty_score":0.008760214},"labels":[],"label_agreement":null},{"id":"W2964022095","doi":"10.1111/rssc.12321","title":"Careful Prior Specification Avoids Incautious Inference for Log-Gaussian Cox Point Processes","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Division of Environmental Biology; Office of International Science and Engineering; Smithsonian Tropical Research Institute; Smithsonian Institution; National Science Foundation","keywords":"Hyperparameter; Point process; Hyperparameter optimization; Inference; Gaussian process; Mathematics; Computer science; Covariate; Gaussian; Algorithm; Statistics; Artificial intelligence","score_opus":0.04243172141445356,"score_gpt":0.24027272705967606,"score_spread":0.1978410056452225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964022095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064856745,0.0001349705,0.99142104,0.00054165145,0.000064454194,0.00004216573,0.0001427804,0.000220916,0.00094636506],"genre_scores_gemma":[0.3226831,0.0005488101,0.66900516,0.00080197764,0.00024306873,0.00051016925,0.001165356,0.00059837825,0.004444112],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9919391,0.005766478,0.00045222536,0.0007744795,0.0007786854,0.00028907135],"domain_scores_gemma":[0.9423471,0.04511117,0.0016291047,0.008081615,0.0024805441,0.00035046914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024498938,0.0007659201,0.0013531728,0.0013465842,0.000961352,0.0023095026,0.002673811,0.0017373378,0.008358458],"category_scores_gemma":[0.09946974,0.0008569485,0.0014227255,0.0020691592,0.0016420761,0.003671405,0.002281471,0.006732574,0.001433823],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017107477,0.00010181175,0.0062750876,0.00024162619,0.00023248437,0.00028928884,0.0003300194,0.20485261,0.0016265897,0.66571033,0.010275364,0.10989376],"study_design_scores_gemma":[0.00005436374,0.00003831995,0.0017358521,0.000115587965,0.00005886329,0.00014143022,0.00009259489,0.4200384,0.0017158905,0.5673551,0.00859926,0.000054316217],"about_ca_topic_score_codex":0.007429449,"about_ca_topic_score_gemma":0.011246942,"teacher_disagreement_score":0.024498938,"about_ca_system_score_codex":0.0015746002,"about_ca_system_score_gemma":0.002762982,"threshold_uncertainty_score":0.12956434},"labels":[],"label_agreement":null},{"id":"W2972425606","doi":"10.1093/jrsssc/qlad060","title":"Confidence tubes for curves on SO(3) and identification of subject-specific gait change after kneeling","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Berlin Mathematics Research Center MATH+; Volkswagen Foundation; Deutsche Forschungsgemeinschaft","keywords":"Kneeling; Gait; Kinematics; Physical medicine and rehabilitation; Gait analysis; Confidence interval; Physical therapy; Computer science; Medicine; Mathematics; Statistics; Physics","score_opus":0.020589285011200345,"score_gpt":0.23598979856640978,"score_spread":0.21540051355520945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972425606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6120521,0.00029290174,0.38504085,0.000118539545,0.000038466176,0.00013729888,0.0009927467,0.0006735285,0.00065365474],"genre_scores_gemma":[0.9728559,0.00006773865,0.025375247,0.000035425808,0.00001908533,0.00013136264,0.001110031,0.00008244795,0.0003226946],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9943098,0.0032170054,0.00036662372,0.00096713874,0.0008396784,0.00029971392],"domain_scores_gemma":[0.80785656,0.16720925,0.012068083,0.006779793,0.0043123947,0.001773994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013818199,0.00051036687,0.0010878044,0.0026482567,0.0002993765,0.0012532179,0.0011012728,0.0014767859,0.0021752776],"category_scores_gemma":[0.124773346,0.0003273898,0.00083072635,0.0012486296,0.001560465,0.0011586479,0.0012520739,0.0012178696,0.00030408808],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003973735,0.00040570844,0.46375695,0.0006165003,0.00085611216,0.0019369134,0.0013960513,0.24303454,0.020323027,0.026971623,0.00369784,0.233031],"study_design_scores_gemma":[0.00002594857,0.0005739206,0.15705161,0.000084758554,0.00008406729,0.0008699039,0.00019463398,0.8230688,0.0044671353,0.012583424,0.0008675171,0.00012823829],"about_ca_topic_score_codex":0.0021132706,"about_ca_topic_score_gemma":0.0010869245,"teacher_disagreement_score":0.013818199,"about_ca_system_score_codex":0.00033717163,"about_ca_system_score_gemma":0.00043496813,"threshold_uncertainty_score":0.07307851},"labels":[],"label_agreement":null},{"id":"W2996907827","doi":"10.1111/rssc.12392","title":"Assessing Heterogeneity in Transition Propensity in Multistate Capture–Recapture Data","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Census and Population Estimation","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Natural Environment Research Council; Sight Research UK","keywords":"Mark and recapture; Homogeneity (statistics); Computer science; Population; Econometrics; Transition (genetics); Machine learning; Mathematics; Biology","score_opus":0.0614124087042399,"score_gpt":0.3326396172780046,"score_spread":0.2712272085737647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996907827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92711264,0.00019941431,0.0711081,0.0002951532,0.000010037547,0.000029808676,0.00046366896,0.00008535244,0.00069569686],"genre_scores_gemma":[0.9966138,0.000013878655,0.0030740588,0.000019091143,0.000004989383,0.0000064054007,0.00019961024,0.000004404715,0.000063647036],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969125,0.0016433884,0.0002497914,0.0006340638,0.0003576195,0.00020261026],"domain_scores_gemma":[0.9288464,0.058590405,0.0056572435,0.004783366,0.0013578705,0.00076467975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0102152,0.00017783279,0.00059604895,0.0013756912,0.0003963422,0.0010138087,0.0009998659,0.0005710574,0.0011793509],"category_scores_gemma":[0.039973166,0.0001670357,0.0005877998,0.001494959,0.0013571661,0.0012073945,0.0009891551,0.0008557857,0.00009523793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029648535,0.00007994092,0.8254487,0.000111388654,0.0007990582,0.0003422089,0.00074870535,0.113542005,0.0021875226,0.013152342,0.0007729759,0.04251877],"study_design_scores_gemma":[0.000040603125,0.00024957006,0.5590886,0.00008183477,0.0002207041,0.00045895603,0.00076322595,0.37608984,0.0027730945,0.058851764,0.0012620268,0.00011977191],"about_ca_topic_score_codex":0.013737689,"about_ca_topic_score_gemma":0.008856751,"teacher_disagreement_score":0.013737689,"about_ca_system_score_codex":0.00091392116,"about_ca_system_score_gemma":0.0006133225,"threshold_uncertainty_score":0.054023743},"labels":[],"label_agreement":null},{"id":"W3098241802","doi":"10.1111/rssc.12449","title":"Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates","year":2020,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"Foundation for Barnes-Jewish Hospital","keywords":"Covariate; Computer science; Baseline (sea); Neurocognitive; Multivariate statistics; Tree (set theory); Biomarker; Machine learning; Artificial intelligence; Data mining; Cognition; Medicine; Mathematics; Biology","score_opus":0.06324134367638613,"score_gpt":0.3150368408390256,"score_spread":0.2517954971626395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098241802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039229166,0.0004662014,0.9582995,0.0004485209,0.00006140711,0.000028447392,0.00041991952,0.00056140905,0.0004853147],"genre_scores_gemma":[0.69340277,0.0008158332,0.30010104,0.0002742092,0.00021653638,0.00022259631,0.0021853412,0.00017793446,0.0026035926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943477,0.00027666922,0.000026576701,0.000115958996,0.00008848759,0.000057462144],"domain_scores_gemma":[0.9969025,0.002120913,0.0002115486,0.00023713388,0.00038461195,0.00014330893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004008012,0.00068690587,0.001084975,0.0009205171,0.0004802302,0.00073114585,0.001158712,0.0008937466,0.0017052053],"category_scores_gemma":[0.007916514,0.00029000625,0.0010530836,0.001227526,0.00030550093,0.00094483554,0.0009017994,0.0016760709,0.0004713502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018926986,0.0000709293,0.017447619,0.00007276544,0.00018785875,0.00014793544,0.00011032857,0.8196118,0.0009660955,0.011080037,0.005583571,0.14453174],"study_design_scores_gemma":[0.0000038677836,0.000018478808,0.00048762799,0.000008205766,0.000011838184,0.00002892007,0.0000053250897,0.9934496,0.00011849304,0.0054418477,0.00042076508,0.0000049707583],"about_ca_topic_score_codex":0.0068515185,"about_ca_topic_score_gemma":0.0077282363,"teacher_disagreement_score":0.0068515185,"about_ca_system_score_codex":0.0005147679,"about_ca_system_score_gemma":0.001077306,"threshold_uncertainty_score":0.021196663},"labels":[],"label_agreement":null},{"id":"W3114010652","doi":"10.1093/jrsssc/qlad014","title":"Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Cluster analysis; Covariate; Computer science; Population; Econometrics; Regression; Invariant (physics); Statistics; Data mining; Artificial intelligence; Mathematics; Demography; Sociology","score_opus":0.19552555781229294,"score_gpt":0.37536051397625736,"score_spread":0.17983495616396442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114010652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3910305,0.0010297106,0.6035275,0.0007026722,0.00015573423,0.00016763722,0.001217639,0.0004116488,0.0017569093],"genre_scores_gemma":[0.92868394,0.00038897523,0.066333145,0.000078751,0.000107310945,0.00012926098,0.0025211365,0.0001479722,0.0016096281],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787474,0.0012943891,0.00010630973,0.0003880735,0.00016619675,0.00017029318],"domain_scores_gemma":[0.99257845,0.003945635,0.00089858635,0.0013001263,0.0010573309,0.00021988322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064906827,0.0005629468,0.0008364652,0.0029608463,0.00047102728,0.0008210013,0.0011237507,0.0006923038,0.0024888169],"category_scores_gemma":[0.021842765,0.00015531019,0.0011160445,0.0020564054,0.0009301472,0.00072963437,0.0013396058,0.000903892,0.00048755654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012539979,0.00020075604,0.19284038,0.0005478638,0.0013238721,0.0008318932,0.0013282932,0.36126462,0.008987099,0.07328097,0.01065512,0.34748507],"study_design_scores_gemma":[0.000025021629,0.00013589908,0.09128902,0.0000817438,0.00014238992,0.000153925,0.00055141083,0.878572,0.0018849189,0.02367695,0.0034180381,0.00006862399],"about_ca_topic_score_codex":0.008508785,"about_ca_topic_score_gemma":0.0048763803,"teacher_disagreement_score":0.008508785,"about_ca_system_score_codex":0.0008938373,"about_ca_system_score_gemma":0.00092396163,"threshold_uncertainty_score":0.034326375},"labels":[],"label_agreement":null},{"id":"W3158310581","doi":"10.1111/rssc.12483","title":"Clustering and Automatic Labelling Within Time Series of Categorical Observations—With an Application to Marine Log Messages","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"NRC Steacie Institute for Molecular Sciences","keywords":"Computer science; Cluster analysis; Data mining; Set (abstract data type); A priori and a posteriori; Categorical variable; Inference; Bayesian probability; Series (stratigraphy); State (computer science); Hierarchical clustering; Identification (biology); Algorithm; Pattern recognition (psychology); Artificial intelligence; Machine learning","score_opus":0.010784333953844564,"score_gpt":0.21812221750340863,"score_spread":0.20733788354956406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158310581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1206421,0.00022882572,0.8750854,0.00039806656,0.00006416943,0.00014155677,0.0006424989,0.0020246522,0.0007727606],"genre_scores_gemma":[0.55344135,0.00012745759,0.44290012,0.000075086755,0.0000937325,0.00015128574,0.0016194783,0.00017579639,0.0014156554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986027,0.0005894562,0.000090461755,0.00037535676,0.00023642657,0.00010560771],"domain_scores_gemma":[0.9896039,0.007093653,0.0008056727,0.0010445308,0.001224282,0.00022806242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023450481,0.00045123536,0.0007579435,0.0028129257,0.0007921253,0.0011489732,0.0015766781,0.0012603357,0.0010807151],"category_scores_gemma":[0.013487772,0.000311937,0.0005653261,0.0026026606,0.0006930814,0.00100523,0.0010454516,0.00125543,0.0006106063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066028314,0.0004948401,0.02397577,0.0002884559,0.00014290222,0.00037563255,0.0012759033,0.37813538,0.010482975,0.01988052,0.0065494673,0.5577378],"study_design_scores_gemma":[0.000009009043,0.000019061308,0.0024130153,0.000008665209,0.000005403604,0.000024107265,0.00006512396,0.9883415,0.0008796931,0.0076112794,0.0006075418,0.000015513413],"about_ca_topic_score_codex":0.012803711,"about_ca_topic_score_gemma":0.0135469,"teacher_disagreement_score":0.012803711,"about_ca_system_score_codex":0.0010741957,"about_ca_system_score_gemma":0.00090818334,"threshold_uncertainty_score":0.025458336},"labels":[],"label_agreement":null},{"id":"W3169192659","doi":"10.1111/rssc.12500","title":"Mixed-Frequency Bayesian Predictive Synthesis for Economic Nowcasting","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Bayesian probability; Computer science; Econometrics; Interdependence; Aggregate (composite); Quarter (Canadian coin); Data mining; Survey of Professional Forecasters; Artificial intelligence; Monetary policy; Economics; Geography","score_opus":0.05204909368515499,"score_gpt":0.3205383504228609,"score_spread":0.2684892567377059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169192659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003341403,0.00022210927,0.99518615,0.00017587826,0.000032154312,0.000023474331,0.00012281934,0.00012765505,0.0007683812],"genre_scores_gemma":[0.557272,0.0010556466,0.43533346,0.00028512438,0.00029522445,0.00048705304,0.0012286166,0.00017420056,0.0038686781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827087,0.0009525997,0.000093468756,0.00028630556,0.00031468694,0.00008217943],"domain_scores_gemma":[0.98930955,0.008811742,0.00059790234,0.00055017794,0.0006200426,0.000110579516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065637706,0.00093034445,0.0013891873,0.001734578,0.0004826369,0.0020119788,0.0016434117,0.0012548347,0.0048210067],"category_scores_gemma":[0.024223253,0.0009648151,0.0013656326,0.0014493619,0.0010993221,0.0023552051,0.0016840886,0.0019119468,0.00058158726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006830473,0.000020554424,0.000599099,0.000094864474,0.00007201996,0.000042459735,0.00008132806,0.87548095,0.0003954602,0.07827552,0.0007718476,0.04409763],"study_design_scores_gemma":[0.000005180073,0.000006610298,0.000064305095,0.00001637855,0.0000074609975,0.0000046585833,0.000005668703,0.969715,0.00014347701,0.029489493,0.0005336937,0.000008035734],"about_ca_topic_score_codex":0.0072853174,"about_ca_topic_score_gemma":0.0053660697,"teacher_disagreement_score":0.0072853174,"about_ca_system_score_codex":0.0013963033,"about_ca_system_score_gemma":0.0013381412,"threshold_uncertainty_score":0.03471291},"labels":[],"label_agreement":null},{"id":"W3173957380","doi":"10.1093/jrsssc/qlac006","title":"Aggregated functional data model applied on clustering and disaggregation of UK electrical load profiles","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Cluster analysis; Similarity (geometry); Computer science; Covariance; Electrical load; Data mining; Electric potential energy; Consumption (sociology); Energy (signal processing); Statistics; Mathematics; Engineering; Artificial intelligence; Voltage; Electrical engineering","score_opus":0.021163486472465232,"score_gpt":0.22504290968525384,"score_spread":0.2038794232127886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173957380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81927586,0.00025586097,0.17075312,0.0006978702,0.000084890955,0.00015146488,0.006116481,0.00056604523,0.0020984032],"genre_scores_gemma":[0.98365927,0.00007653865,0.01113725,0.000031483884,0.000015320376,0.00008900953,0.0037687176,0.000020902526,0.0012014798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927634,0.00034097905,0.00004468136,0.00018114668,0.00007371433,0.000083114806],"domain_scores_gemma":[0.99743503,0.001494942,0.00021971762,0.0002779664,0.0004889073,0.00008339938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027759336,0.0006259501,0.00073651486,0.0012443513,0.00031391706,0.00095474394,0.0011036688,0.0010318063,0.0017814975],"category_scores_gemma":[0.0070153554,0.00029706952,0.00091362203,0.0014956383,0.00036702858,0.00065369345,0.00074595964,0.0008544376,0.00039069625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008265363,0.000029137296,0.0064741243,0.000027081716,0.0000477578,0.000068181864,0.000057769474,0.98264897,0.00022613049,0.0019564186,0.00082699716,0.007554718],"study_design_scores_gemma":[0.0000018026249,0.00000698571,0.0013698093,0.0000022401832,0.0000030680617,0.000003750717,0.000010685552,0.99805945,0.000028376246,0.00041855004,0.000092739865,0.0000025576496],"about_ca_topic_score_codex":0.075076625,"about_ca_topic_score_gemma":0.03577484,"teacher_disagreement_score":0.075076625,"about_ca_system_score_codex":0.0016302848,"about_ca_system_score_gemma":0.0006715222,"threshold_uncertainty_score":0.1492793},"labels":[],"label_agreement":null},{"id":"W3205192946","doi":"10.1093/jrsssc/qlac007","title":"Dynamical non-Gaussian modelling of spatial processes","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Gaussian process; Transformation (genetics); Inference; Gaussian; Statistical physics; Scale (ratio); Variance (accounting); Multivariate statistics; Computer science; State space; State-space representation; Covariate; Econometrics; Applied mathematics; Mathematics; Algorithm; Statistics; Artificial intelligence; Machine learning; Physics","score_opus":0.011222446805052659,"score_gpt":0.22051233874749573,"score_spread":0.20928989194244307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205192946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054172635,0.00018893869,0.94367933,0.0003399739,0.00005034842,0.000024525623,0.00021237358,0.00015195367,0.0011799928],"genre_scores_gemma":[0.95452565,0.000428505,0.039066773,0.000090970854,0.000082008766,0.00008862181,0.00034965604,0.00006566673,0.005302181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882895,0.0004533234,0.0000635814,0.0003472726,0.00019173205,0.00011517243],"domain_scores_gemma":[0.99640757,0.0021914192,0.0006162088,0.000322933,0.00035854743,0.00010327869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025642044,0.0006062147,0.0008823141,0.0010159498,0.00039584376,0.0014578112,0.0015898569,0.00093105383,0.0015041585],"category_scores_gemma":[0.008375988,0.0005278433,0.0011482424,0.0013251303,0.0015352849,0.0017172514,0.00120886,0.0013736044,0.00024087999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002551713,0.000021605898,0.00231861,0.000032423683,0.000049434133,0.00007987792,0.00012798562,0.8382704,0.00086417946,0.15185332,0.00033717274,0.006019388],"study_design_scores_gemma":[0.0000022719075,0.000004325172,0.00029399464,0.0000025436318,0.0000039670067,0.000007949832,0.000006133343,0.9801192,0.000060811664,0.01925291,0.00024104548,0.0000048224424],"about_ca_topic_score_codex":0.017368697,"about_ca_topic_score_gemma":0.0118977325,"teacher_disagreement_score":0.017368697,"about_ca_system_score_codex":0.001226108,"about_ca_system_score_gemma":0.0007706672,"threshold_uncertainty_score":0.03453523},"labels":[],"label_agreement":null},{"id":"W3207858151","doi":"10.1111/rssc.12525","title":"Daily Mortality/Morbidity and Air Quality: Using Multivariate Time Series with Seasonally Varying Covariances","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University; Health Canada; University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada; Natural Resources Canada; Environment and Climate Change Canada","keywords":"Multivariate statistics; Nitrogen dioxide; Environmental science; Ozone; Logistic regression; Particulates; Pollutant; Environmental health; Medicine; Demography; Statistics; Atmospheric sciences; Meteorology; Mathematics; Geography; Chemistry","score_opus":0.03758242231409545,"score_gpt":0.3088720866624708,"score_spread":0.2712896643483753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207858151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91376764,0.0005057778,0.08390702,0.000496725,0.000043963708,0.00003741435,0.0004968219,0.00012139001,0.0006231335],"genre_scores_gemma":[0.9917604,0.00020455192,0.007068691,0.000030413583,0.0000427405,0.00001819691,0.00037620202,0.000015120942,0.00048364248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807405,0.0011954651,0.00005820758,0.00029712706,0.00020985719,0.00016530468],"domain_scores_gemma":[0.9922937,0.005055427,0.0013574775,0.0005958459,0.00048327836,0.00021414804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058419155,0.0007564159,0.0006874438,0.001136851,0.00037591904,0.001050507,0.0009904957,0.0006923978,0.0008938612],"category_scores_gemma":[0.012407028,0.0003755879,0.0017375943,0.0014952559,0.0008840633,0.0007823022,0.0007570819,0.0009772722,0.00010769407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048699533,0.00028211833,0.36945215,0.00006725226,0.0013449464,0.00023381985,0.00017740286,0.5894829,0.001302051,0.0071950364,0.0006194903,0.029355874],"study_design_scores_gemma":[0.000019769084,0.000080224236,0.045481913,0.000010447095,0.00013236204,0.000027039077,0.00004933416,0.9514218,0.00018636762,0.002295394,0.00027088748,0.000024455556],"about_ca_topic_score_codex":0.16383356,"about_ca_topic_score_gemma":0.09599255,"teacher_disagreement_score":0.16383356,"about_ca_system_score_codex":0.0014208766,"about_ca_system_score_gemma":0.0015789436,"threshold_uncertainty_score":0.3257599},"labels":[],"label_agreement":null},{"id":"W4200348404","doi":"10.1111/rssc.12533","title":"Ranking Tailoring Variables for Constructing Individualized Treatment Rules: An Application to Schizophrenia","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of Environmental Health Sciences; National Institute of Mental Health; National Institutes of Health; University of Washington","keywords":"Schizophrenia (object-oriented programming); Ranking (information retrieval); Matching (statistics); Intervention (counseling); Antipsychotic; Medicine; Selection (genetic algorithm); Computer science; Psychiatry; Intensive care medicine; Machine learning","score_opus":0.05566241879820685,"score_gpt":0.35889085328848286,"score_spread":0.30322843449027603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200348404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05899483,0.0013916057,0.9354948,0.001926855,0.000071753544,0.00040685938,0.00022870241,0.00025089242,0.0012336682],"genre_scores_gemma":[0.4275911,0.0010292117,0.56876194,0.0005313786,0.00011811843,0.000512972,0.00021568123,0.00008377784,0.0011558471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9774883,0.0200864,0.0005060496,0.0008526047,0.0007926663,0.00027392537],"domain_scores_gemma":[0.86315346,0.1281322,0.003539933,0.0025852134,0.0018434213,0.00074582064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03763595,0.0010885571,0.0029201964,0.0024242846,0.0012651411,0.001932821,0.0016801466,0.002030962,0.0034172856],"category_scores_gemma":[0.12272336,0.0006792736,0.0020116514,0.0029369185,0.002038986,0.0019222888,0.0026945497,0.00388351,0.00028242636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012669613,0.000532543,0.016266856,0.0004414705,0.00073820085,0.00042629713,0.000829328,0.5120113,0.00088233827,0.13723311,0.0030608128,0.32631075],"study_design_scores_gemma":[0.00043807793,0.00035096417,0.0018522324,0.00009724328,0.00015926777,0.000084614396,0.00015136138,0.7849966,0.00053271966,0.20935635,0.0019119817,0.00006858873],"about_ca_topic_score_codex":0.01089396,"about_ca_topic_score_gemma":0.013484219,"teacher_disagreement_score":0.03763595,"about_ca_system_score_codex":0.0017754837,"about_ca_system_score_gemma":0.003659069,"threshold_uncertainty_score":0.1990403},"labels":[],"label_agreement":null},{"id":"W4206888931","doi":"10.1111/rssc.12547","title":"Zero-State Coupled Markov Switching Count Models for Spatio-Temporal Infectious Disease Spread","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Negative binomial distribution; Count data; Markov chain; Bayesian probability; Statistics; Infectious disease (medical specialty); Inference; Overdispersion; Autoregressive model; Econometrics; Markov model; Bayesian inference; Mathematics; Zero (linguistics); Computer science; Disease; Poisson distribution; Artificial intelligence; Medicine","score_opus":0.05572019150859193,"score_gpt":0.3288504543779682,"score_spread":0.2731302628693763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206888931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08897865,0.00045191584,0.90662766,0.000973181,0.00009173775,0.00007440664,0.00050481444,0.00025763724,0.00203991],"genre_scores_gemma":[0.9297839,0.0007757443,0.059463866,0.00021833763,0.00017648368,0.00037683974,0.00089570263,0.0000990164,0.008210173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972543,0.0017084079,0.000117762145,0.0004534815,0.00026054916,0.00020541552],"domain_scores_gemma":[0.97548217,0.020660385,0.0019317381,0.00062245684,0.0009382183,0.0003650998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009358516,0.0010236903,0.0016685011,0.0019888869,0.00064098556,0.0017671913,0.003322439,0.0018455092,0.0041186335],"category_scores_gemma":[0.023331493,0.00087472063,0.0015087884,0.0015125177,0.002565335,0.0023618594,0.001681502,0.0025077178,0.00064605614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121899124,0.00006195217,0.0060787434,0.00009515421,0.00011497597,0.00021342955,0.00031618288,0.73066115,0.00069735886,0.25317463,0.0010278773,0.007436601],"study_design_scores_gemma":[0.000011048739,0.000016585804,0.00034712907,0.000009872565,0.000014424488,0.00001988348,0.000018349552,0.95911545,0.000054215496,0.0401411,0.00023947522,0.000012436702],"about_ca_topic_score_codex":0.012440821,"about_ca_topic_score_gemma":0.009190154,"teacher_disagreement_score":0.012440821,"about_ca_system_score_codex":0.0017931713,"about_ca_system_score_gemma":0.0010296361,"threshold_uncertainty_score":0.049493134},"labels":[],"label_agreement":null},{"id":"W4213293529","doi":"10.1111/rssc.12542","title":"Lifting Scheme for Streamflow Data in River Networks","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Global Water Futures; Canada First Research Excellence Fund; National Research Foundation of Korea","keywords":"Streamflow; Smoothing; Computer science; Scheme (mathematics); Domain (mathematical analysis); Data mining; Algorithm; Drainage basin; Mathematics; Geography","score_opus":0.012843197039650086,"score_gpt":0.23321852734741033,"score_spread":0.22037533030776024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213293529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018956399,0.000036784284,0.9806348,0.00003339303,0.000020233643,0.000016230986,0.000034005596,0.00010197307,0.00016621828],"genre_scores_gemma":[0.32539955,0.00015593144,0.6723782,0.00004800322,0.00006330615,0.00009591526,0.00023194498,0.00006035843,0.0015667939],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996897,0.000086637694,0.000025882127,0.000053706044,0.00011646387,0.00002771609],"domain_scores_gemma":[0.99940777,0.0002214772,0.00007755527,0.00012600809,0.0001280111,0.00003928844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007633341,0.00027219122,0.0004026539,0.0005723267,0.00021145871,0.00036020402,0.0005814374,0.00037582102,0.0010580195],"category_scores_gemma":[0.0019642576,0.00016019396,0.00056205085,0.0006608564,0.00034007675,0.00061661325,0.00070422306,0.0006741388,0.00024526866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022787158,0.00009013273,0.0034816435,0.00017323672,0.000073559706,0.00022681095,0.00020954349,0.43476155,0.08112695,0.03577334,0.0018118998,0.44204345],"study_design_scores_gemma":[0.00000431332,0.000014014329,0.0002911147,0.0000025589109,0.0000025071208,0.000012698123,0.0000051198795,0.99573493,0.0015856473,0.0018438135,0.0004987604,0.000004563244],"about_ca_topic_score_codex":0.0019893243,"about_ca_topic_score_gemma":0.001460333,"teacher_disagreement_score":0.0019893243,"about_ca_system_score_codex":0.00022805744,"about_ca_system_score_gemma":0.00041669226,"threshold_uncertainty_score":0.0040369034},"labels":[],"label_agreement":null},{"id":"W4221027384","doi":"10.1111/rssc.12543","title":"A Semi-Parametric Integer-Valued Autoregressive Model with Covariates","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Covariate; Negative binomial distribution; Econometrics; Overdispersion; Count data; Parametric statistics; Autoregressive model; Parametric model; Poisson distribution; Statistics; Mathematics; Computer science","score_opus":0.016128657541014984,"score_gpt":0.21207055597715552,"score_spread":0.19594189843614054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221027384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09168334,0.0010085441,0.89710814,0.0027183262,0.00020563579,0.00012298282,0.0019246778,0.0005107859,0.0047175884],"genre_scores_gemma":[0.91516954,0.0011237704,0.060002405,0.00039410638,0.0002401741,0.00033328542,0.0014946185,0.00008678502,0.021155227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975516,0.001291959,0.00011003592,0.0005282195,0.00025567508,0.00026260008],"domain_scores_gemma":[0.9915781,0.005888844,0.0012419436,0.00041473538,0.0006507918,0.00022543888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059409034,0.00085730903,0.0020699825,0.000994226,0.0005188524,0.003248087,0.004047101,0.002415041,0.005802661],"category_scores_gemma":[0.011219026,0.0009214806,0.0012623444,0.002127453,0.0014030514,0.0024569924,0.0014063637,0.0031016173,0.0009812345],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012565301,0.000106900596,0.005565794,0.00012807705,0.000120428034,0.00043408066,0.0002475965,0.8145315,0.00056959764,0.1626233,0.0017934426,0.013753701],"study_design_scores_gemma":[0.000016605318,0.000028854434,0.0005106222,0.000014571001,0.000026742244,0.00003341072,0.000023856373,0.98179835,0.00006236672,0.016750878,0.0007151673,0.00001856222],"about_ca_topic_score_codex":0.0146665275,"about_ca_topic_score_gemma":0.00945329,"teacher_disagreement_score":0.0146665275,"about_ca_system_score_codex":0.0012523188,"about_ca_system_score_gemma":0.0013824886,"threshold_uncertainty_score":0.03141886},"labels":[],"label_agreement":null},{"id":"W4225130189","doi":"10.1111/rssc.12572","title":"Stopping Time Detection of Wood Panel Compression: A Functional Time-Series Approach","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Simon Fraser University; British Columbia Institute of Technology","funders":"","keywords":"Univariate; Computer science; Sample (material); Series (stratigraphy); Algorithm; Statistics; Mathematics; Multivariate statistics","score_opus":0.04960986584693204,"score_gpt":0.29326835102592713,"score_spread":0.24365848517899508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225130189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11377786,0.00026059448,0.88464767,0.00028039122,0.00002236801,0.000045085962,0.000061032264,0.00011763154,0.0007873412],"genre_scores_gemma":[0.92961955,0.0001623849,0.06867415,0.000068120295,0.00004504443,0.00009693459,0.00020838309,0.000058999318,0.0010665532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721056,0.0016147584,0.00014154684,0.00045866295,0.00039340096,0.00018109953],"domain_scores_gemma":[0.9636368,0.030702503,0.0026012966,0.0007813493,0.0017615545,0.00051640265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012168359,0.0010870378,0.0013173498,0.0020119261,0.0004915583,0.0012281144,0.0020327673,0.0018421437,0.001702207],"category_scores_gemma":[0.03601445,0.00053572055,0.0010620984,0.0009794487,0.001487021,0.0015738822,0.001034285,0.0020813122,0.0001758619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002399118,0.00012888643,0.011301959,0.00015993373,0.00015711458,0.0002499856,0.00020022664,0.9254985,0.003314092,0.031436045,0.000430635,0.02688267],"study_design_scores_gemma":[0.0000024076674,0.000021940768,0.00061986496,0.000007546227,0.000008008516,0.000012228613,0.000011804301,0.9962851,0.00049799425,0.0024624236,0.00006307283,0.000007686496],"about_ca_topic_score_codex":0.0032088768,"about_ca_topic_score_gemma":0.001527349,"teacher_disagreement_score":0.012168359,"about_ca_system_score_codex":0.0010230088,"about_ca_system_score_gemma":0.00084967347,"threshold_uncertainty_score":0.06435323},"labels":[],"label_agreement":null},{"id":"W4232028169","doi":"10.1111/rssc.12303","title":"Issue Information","year":2019,"lang":"en","type":"paratext","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Human auditory perception and evaluation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Citation; Computer science; Information retrieval; Library science","score_opus":0.008294991460936646,"score_gpt":0.23734726654723542,"score_spread":0.22905227508629877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232028169","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000325827,0.0014736654,0.0036388931,0.0044113323,0.017077455,0.0005829524,0.05349688,0.0055413465,0.91345173],"genre_scores_gemma":[0.0006868028,0.0006622088,0.001070892,0.0011572065,0.0017110234,0.00017066525,0.014855738,0.0011761789,0.97850925],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986395,0.00015619729,0.0001280792,0.00022674528,0.0007159303,0.00013352215],"domain_scores_gemma":[0.99366885,0.0010046102,0.00029496316,0.00071809435,0.0028516718,0.001461817],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014955655,0.0015008028,0.0016944751,0.005504555,0.0011005863,0.0075389524,0.001842727,0.0021057362,0.92747396],"category_scores_gemma":[0.012511272,0.0006080765,0.0008747557,0.0045127543,0.0004972761,0.0032305508,0.0023541555,0.0019903167,0.90704286],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014276019,0.000019335894,0.00003948297,0.00011997316,0.0000019715405,0.000010709378,0.000005016131,0.000033306835,0.000086241445,0.0010049685,0.956195,0.042469777],"study_design_scores_gemma":[0.000016389393,0.000016512218,0.0002492561,0.00009955218,0.000002505507,0.000028056962,0.000013381532,0.00009707861,0.00008742598,0.002032958,0.99735034,0.0000065333597],"about_ca_topic_score_codex":0.0014655266,"about_ca_topic_score_gemma":0.0030713184,"teacher_disagreement_score":0.07252604,"about_ca_system_score_codex":0.0012357385,"about_ca_system_score_gemma":0.002801842,"threshold_uncertainty_score":0.10344958},"labels":[],"label_agreement":null},{"id":"W4236265681","doi":"10.1111/rssc.12304","title":"Issue Information","year":2019,"lang":"en","type":"paratext","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Human auditory perception and evaluation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.008294991460936646,"score_gpt":0.23734726654723542,"score_spread":0.22905227508629877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236265681","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000325827,0.0014736654,0.0036388931,0.0044113323,0.017077455,0.0005829524,0.05349688,0.0055413465,0.91345173],"genre_scores_gemma":[0.0006868028,0.0006622088,0.001070892,0.0011572065,0.0017110234,0.00017066525,0.014855738,0.0011761789,0.97850925],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986395,0.00015619729,0.0001280792,0.00022674528,0.0007159303,0.00013352215],"domain_scores_gemma":[0.99366885,0.0010046102,0.00029496316,0.00071809435,0.0028516718,0.001461817],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014955655,0.0015008028,0.0016944751,0.005504555,0.0011005863,0.0075389524,0.001842727,0.0021057362,0.92747396],"category_scores_gemma":[0.012511272,0.0006080765,0.0008747557,0.0045127543,0.0004972761,0.0032305508,0.0023541555,0.0019903167,0.90704286],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014276019,0.000019335894,0.00003948297,0.00011997316,0.0000019715405,0.000010709378,0.000005016131,0.000033306835,0.000086241445,0.0010049685,0.956195,0.042469777],"study_design_scores_gemma":[0.000016389393,0.000016512218,0.0002492561,0.00009955218,0.000002505507,0.000028056962,0.000013381532,0.00009707861,0.00008742598,0.002032958,0.99735034,0.0000065333597],"about_ca_topic_score_codex":0.0014655266,"about_ca_topic_score_gemma":0.0030713184,"teacher_disagreement_score":0.07252604,"about_ca_system_score_codex":0.0012357385,"about_ca_system_score_gemma":0.002801842,"threshold_uncertainty_score":0.10344958},"labels":[],"label_agreement":null},{"id":"W4237867045","doi":"10.1111/rssc.12182","title":"Applied Statistics","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Statistics; Mathematics","score_opus":0.055610542923033905,"score_gpt":0.36940063095721554,"score_spread":0.3137900880341816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237867045","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004733757,0.06051086,0.5186178,0.06099425,0.03394522,0.0039285924,0.04225877,0.0081904195,0.2668204],"genre_scores_gemma":[0.17428721,0.09690076,0.4192903,0.033540852,0.03662858,0.019160178,0.060322914,0.007923605,0.15194562],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9333695,0.029581537,0.0082680825,0.007931461,0.019367844,0.0014816732],"domain_scores_gemma":[0.8300243,0.09520062,0.011018394,0.026590178,0.034354273,0.0028122542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031589888,0.0019378121,0.0034885777,0.0085365055,0.0016989909,0.010582599,0.003138405,0.0037277937,0.08915868],"category_scores_gemma":[0.19411239,0.0009286569,0.0025058803,0.009513931,0.004521468,0.0057144677,0.005157614,0.0064738505,0.053905237],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019967517,0.0000992482,0.0037631697,0.003589462,0.00046897563,0.00018344799,0.0006354754,0.0029285634,0.00025805936,0.13814619,0.51252526,0.3372024],"study_design_scores_gemma":[0.000072273724,0.00020435618,0.0028312826,0.0034503136,0.000110246416,0.00050781976,0.00047379985,0.0043635545,0.0002639594,0.18824148,0.7993796,0.00010139227],"about_ca_topic_score_codex":0.0027104085,"about_ca_topic_score_gemma":0.0018567983,"teacher_disagreement_score":0.08915868,"about_ca_system_score_codex":0.004077904,"about_ca_system_score_gemma":0.013591571,"threshold_uncertainty_score":0.29826552},"labels":[],"label_agreement":null},{"id":"W4250628122","doi":"10.1111/rssc.12423","title":"Applied Statistics","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cancer Care Ontario","funders":"","keywords":"Statistics; Computer science; Mathematics","score_opus":0.04161642025266121,"score_gpt":0.3442702138733612,"score_spread":0.3026537936207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250628122","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045185364,0.06267249,0.49611846,0.06296145,0.03440764,0.0034922704,0.038234394,0.007854328,0.28974047],"genre_scores_gemma":[0.17482771,0.10120466,0.4057509,0.033703838,0.037476547,0.017272493,0.05600205,0.00756597,0.16619581],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9388122,0.026536401,0.0075945575,0.0074413004,0.018211782,0.0014037951],"domain_scores_gemma":[0.8466592,0.083184294,0.010224751,0.025304101,0.031872388,0.0027552843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02974964,0.0019425609,0.0034527902,0.008368594,0.00174908,0.010536984,0.0030607644,0.0037756772,0.09082357],"category_scores_gemma":[0.17687985,0.00092898164,0.0023941123,0.009237323,0.0047117905,0.005747905,0.0051693614,0.006360192,0.055707067],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018200702,0.00009570908,0.0037084296,0.003324061,0.00042621364,0.00018928859,0.00061772653,0.002772698,0.00027099927,0.15242603,0.50609666,0.32989016],"study_design_scores_gemma":[0.00006391757,0.0001834852,0.002658614,0.0030922908,0.00009636661,0.00048895425,0.0004436744,0.0039289948,0.000253486,0.19046107,0.7982357,0.00009342488],"about_ca_topic_score_codex":0.0027266378,"about_ca_topic_score_gemma":0.0018417386,"teacher_disagreement_score":0.09082357,"about_ca_system_score_codex":0.0040459805,"about_ca_system_score_gemma":0.013258365,"threshold_uncertainty_score":0.3038351},"labels":[],"label_agreement":null},{"id":"W4251427285","doi":"10.1111/rssc.12248","title":"Applied Statistics","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Statistics; Mathematics","score_opus":0.04263095897102618,"score_gpt":0.3528086313428191,"score_spread":0.3101776723717929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251427285","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046633026,0.061499033,0.5198782,0.06138324,0.0342249,0.0039216457,0.041980475,0.008228001,0.26422113],"genre_scores_gemma":[0.17232692,0.098422855,0.41992733,0.033656776,0.037281286,0.019223958,0.060071636,0.008024926,0.15106425],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.93279034,0.029762402,0.008364707,0.008013556,0.019589627,0.0014793923],"domain_scores_gemma":[0.82717586,0.09689724,0.011210487,0.027058436,0.03481001,0.002847942],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.03182955,0.001955279,0.003524329,0.008616778,0.0017050202,0.010630959,0.0031657561,0.0037531308,0.088608],"category_scores_gemma":[0.19604085,0.0009361865,0.002508863,0.009555423,0.0045818477,0.005762705,0.0051835817,0.0065101236,0.05395953],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019633444,0.00009817233,0.0037119454,0.0036121819,0.00047041493,0.00018354584,0.000635838,0.0029321106,0.0002569584,0.13813516,0.51441896,0.33534834],"study_design_scores_gemma":[0.00007141371,0.00020010906,0.0027666374,0.0034544915,0.000109301174,0.0005032598,0.00046636633,0.004344016,0.00026029095,0.18893339,0.7987896,0.00010113306],"about_ca_topic_score_codex":0.002699706,"about_ca_topic_score_gemma":0.0018437569,"teacher_disagreement_score":0.911392,"about_ca_system_score_codex":0.0040863925,"about_ca_system_score_gemma":0.013688017,"threshold_uncertainty_score":0.29642326},"labels":[],"label_agreement":null},{"id":"W4256548958","doi":"10.1111/rssc.12422","title":"Applied Statistics","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cancer Care Ontario","funders":"","keywords":"Statistics; Computer science; Mathematics","score_opus":0.04161642025266121,"score_gpt":0.3442702138733612,"score_spread":0.3026537936207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256548958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045185364,0.06267249,0.49611846,0.06296145,0.03440764,0.0034922704,0.038234394,0.007854328,0.28974047],"genre_scores_gemma":[0.17482771,0.10120466,0.4057509,0.033703838,0.037476547,0.017272493,0.05600205,0.00756597,0.16619581],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9388122,0.026536401,0.0075945575,0.0074413004,0.018211782,0.0014037951],"domain_scores_gemma":[0.8466592,0.083184294,0.010224751,0.025304101,0.031872388,0.0027552843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02974964,0.0019425609,0.0034527902,0.008368594,0.00174908,0.010536984,0.0030607644,0.0037756772,0.09082357],"category_scores_gemma":[0.17687985,0.00092898164,0.0023941123,0.009237323,0.0047117905,0.005747905,0.0051693614,0.006360192,0.055707067],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018200702,0.00009570908,0.0037084296,0.003324061,0.00042621364,0.00018928859,0.00061772653,0.002772698,0.00027099927,0.15242603,0.50609666,0.32989016],"study_design_scores_gemma":[0.00006391757,0.0001834852,0.002658614,0.0030922908,0.00009636661,0.00048895425,0.0004436744,0.0039289948,0.000253486,0.19046107,0.7982357,0.00009342488],"about_ca_topic_score_codex":0.0027266378,"about_ca_topic_score_gemma":0.0018417386,"teacher_disagreement_score":0.09082357,"about_ca_system_score_codex":0.0040459805,"about_ca_system_score_gemma":0.013258365,"threshold_uncertainty_score":0.3038351},"labels":[],"label_agreement":null},{"id":"W4281398297","doi":"10.1111/rssc.12567","title":"Non-Separable Spatio-Temporal Models via Transformed Multivariate Gaussian Markov Random Fields","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Population and Public Health; University of British Columbia","funders":"","keywords":"Random field; Multivariate statistics; Computer science; Gaussian; Separable space; Markov random field; Poisson distribution; Temporal database; Markov chain; Econometrics; Statistical physics; Mathematics; Artificial intelligence; Data mining; Statistics; Machine learning; Physics","score_opus":0.007817243642473616,"score_gpt":0.2171668493297148,"score_spread":0.2093496056872412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281398297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08834999,0.00029913106,0.9076634,0.0006918166,0.000056790523,0.000059914542,0.0006007042,0.00029524375,0.0019829632],"genre_scores_gemma":[0.9418715,0.00051922747,0.049587384,0.00017513518,0.000091044894,0.00021079631,0.00072111073,0.00010368157,0.006720023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988397,0.00049962767,0.00004910235,0.00027265676,0.00015033748,0.00018859707],"domain_scores_gemma":[0.9948437,0.0032781393,0.0009284487,0.000302249,0.00044095612,0.00020644642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036214706,0.0007786849,0.0010063344,0.0013219406,0.0004278039,0.001422758,0.0020832324,0.0014780563,0.0026405097],"category_scores_gemma":[0.0080117965,0.000656078,0.0016266877,0.0014727069,0.001878033,0.0021895731,0.001291537,0.0018903031,0.00041624325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056769775,0.000034689165,0.002612446,0.00003278953,0.000051946343,0.000110811976,0.00014198924,0.8393648,0.00046097548,0.15106562,0.000740343,0.005326864],"study_design_scores_gemma":[0.000006687223,0.0000072817024,0.00026100376,0.000004142912,0.0000071815443,0.000012623298,0.000010924804,0.9764737,0.000047973288,0.022936262,0.00022484016,0.000007358444],"about_ca_topic_score_codex":0.029269781,"about_ca_topic_score_gemma":0.016582359,"teacher_disagreement_score":0.029269781,"about_ca_system_score_codex":0.002176867,"about_ca_system_score_gemma":0.0014715972,"threshold_uncertainty_score":0.05819881},"labels":[],"label_agreement":null},{"id":"W4292168321","doi":"10.1111/rssc.12583","title":"Statistical Integration of Heterogeneous Omics Data: Probabilistic Two-Way Partial Least Squares (PO2PLS)","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Medical Research Council; Institute of Genetics; Universitair Medisch Centrum Utrecht; E-Rare","keywords":"Partial least squares regression; Probabilistic logic; Data integration; Omics; Computer science; Statistical model; Data mining; Statistics; Computational biology; Data science; Mathematics; Machine learning; Biology; Artificial intelligence; Bioinformatics","score_opus":0.022616889318122474,"score_gpt":0.28201730529210517,"score_spread":0.25940041597398267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292168321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056364834,0.00020803671,0.9932411,0.00018667667,0.000025431196,0.000048554874,0.00014682914,0.00039017154,0.00011675902],"genre_scores_gemma":[0.224004,0.00038839522,0.77217144,0.00040501694,0.00012134527,0.000574642,0.001277908,0.00039308937,0.00066422933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933715,0.0039941454,0.00025534633,0.0013269692,0.00092848414,0.00012357435],"domain_scores_gemma":[0.9895654,0.007124156,0.0010731445,0.0012627272,0.000778784,0.0001958573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012822397,0.0016116159,0.0017029007,0.0015775483,0.000918345,0.0019089682,0.0021627727,0.0011196854,0.001176222],"category_scores_gemma":[0.029301127,0.0011755063,0.0025482045,0.0032133271,0.0014963769,0.0015013738,0.0034636194,0.0024899866,0.00037904165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005168152,0.00025143978,0.022197379,0.0013087756,0.0041534775,0.0007172108,0.00047605435,0.56463116,0.017744202,0.050725818,0.010388439,0.32688916],"study_design_scores_gemma":[0.000055706918,0.00012391953,0.0033724844,0.000032125015,0.00015558965,0.00018549239,0.000055783472,0.91762555,0.0025859436,0.07177317,0.0039691674,0.00006508539],"about_ca_topic_score_codex":0.002958024,"about_ca_topic_score_gemma":0.003015233,"teacher_disagreement_score":0.012822397,"about_ca_system_score_codex":0.0005858855,"about_ca_system_score_gemma":0.002678777,"threshold_uncertainty_score":0.067812145},"labels":[],"label_agreement":null},{"id":"W4293547557","doi":"10.1111/rssc.12589","title":"Non-Parametric Bayesian Covariate-Dependent Multivariate Functional Clustering: An Application to Time-Series Data for Multiple Air Pollutants","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ottawa Public Health; University of Ottawa; Health Canada","funders":"National Research Foundation of Korea","keywords":"Multivariate statistics; Cluster analysis; Covariate; Bayesian probability; Environmental science; Air quality index; Air pollution; Statistics; Econometrics; Parametric statistics; Computer science; Geography; Mathematics; Meteorology; Ecology","score_opus":0.03667557134290798,"score_gpt":0.29862143345226044,"score_spread":0.26194586210935245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293547557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044252083,0.00019328781,0.9542799,0.00041126498,0.000023756393,0.00010125138,0.00018309201,0.00024045078,0.0003149386],"genre_scores_gemma":[0.6660717,0.0003655036,0.33041483,0.0001312682,0.00008178067,0.00036694814,0.0007925827,0.00011130606,0.0016639868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618775,0.002791902,0.00013699514,0.0004401791,0.00029909908,0.00014400545],"domain_scores_gemma":[0.98156136,0.0141463205,0.0013672793,0.0011434935,0.0014823683,0.00029914675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0129202055,0.00070459966,0.0012766406,0.0016257516,0.00097435154,0.0008971808,0.0025187107,0.001848603,0.0014980907],"category_scores_gemma":[0.030888377,0.0004944734,0.0022484162,0.0021826727,0.0012624129,0.0014047956,0.0017979454,0.0016455029,0.0002684639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018925712,0.000119954886,0.010420128,0.00012760829,0.00024742208,0.0002196885,0.00047993855,0.88809276,0.0010075386,0.045604564,0.0012572993,0.052233785],"study_design_scores_gemma":[0.000007570007,0.000020487827,0.0012599911,0.000006256931,0.000009270986,0.000022528486,0.000024641022,0.99119884,0.000064912834,0.007152837,0.00022005702,0.000012673157],"about_ca_topic_score_codex":0.033491664,"about_ca_topic_score_gemma":0.030320618,"teacher_disagreement_score":0.033491664,"about_ca_system_score_codex":0.0016888324,"about_ca_system_score_gemma":0.0019435133,"threshold_uncertainty_score":0.068329394},"labels":[],"label_agreement":null},{"id":"W4297198601","doi":"10.1111/rssc.12595","title":"A Bayesian Model for Estimating Sustainable Development Goal Indicator 4.1.2: School Completion Rates","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Research University Higher School of Economics; European Commission","keywords":"Bayesian probability; Econometrics; Sustainable development; Computer science; Statistics; Economics; Mathematics; Artificial intelligence; Political science","score_opus":0.011441223784588084,"score_gpt":0.2649205373880311,"score_spread":0.25347931360344306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297198601","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11755352,0.0009048992,0.8665971,0.0022945285,0.00012886939,0.00046241228,0.00591704,0.00067686813,0.0054647587],"genre_scores_gemma":[0.77114,0.0017328331,0.19785866,0.0005159663,0.0002562409,0.0018371756,0.009917906,0.00024171716,0.016499635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956564,0.002781974,0.00015780823,0.00068978895,0.00042262874,0.00029129974],"domain_scores_gemma":[0.97477573,0.019711742,0.002089617,0.0010599727,0.0020163138,0.00034652388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01819259,0.0010599096,0.0017877027,0.002297962,0.0005293354,0.0017883001,0.0032331946,0.0022402615,0.0069251833],"category_scores_gemma":[0.045163065,0.0010507852,0.0016465856,0.0025855002,0.001217474,0.0022374112,0.001445976,0.0030052771,0.0017522925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029033542,0.00015091334,0.02978492,0.00017520851,0.0002614927,0.00015121831,0.00043345912,0.8148754,0.00030559252,0.083867654,0.0076870616,0.062016718],"study_design_scores_gemma":[0.000053152362,0.00007437707,0.0055945194,0.00010033558,0.00006000232,0.0000610443,0.00008055236,0.9573899,0.0001447711,0.03333537,0.0030546612,0.000051340274],"about_ca_topic_score_codex":0.040399678,"about_ca_topic_score_gemma":0.022649702,"teacher_disagreement_score":0.040399678,"about_ca_system_score_codex":0.0018772627,"about_ca_system_score_gemma":0.0019405005,"threshold_uncertainty_score":0.096212745},"labels":[],"label_agreement":null},{"id":"W4362630870","doi":"10.1093/jrsssc/qlad022","title":"Longitudinal canonical correlation analysis","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Canonical correlation; Canonical analysis; Correlation; Multivariate statistics; Longitudinal data; Longitudinal study; Statistics; Multivariate analysis; Mathematics; Latent variable; Applied mathematics; Computer science; Data mining; Geometry","score_opus":0.026118028036882856,"score_gpt":0.26987768471313045,"score_spread":0.2437596566762476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362630870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038612522,0.00160347,0.9512123,0.0006303497,0.0002269393,0.00011509711,0.0009591874,0.00047517716,0.0061650365],"genre_scores_gemma":[0.7095818,0.0029724864,0.27288598,0.00044621428,0.0007049126,0.00061657315,0.0030316338,0.00041308318,0.009347431],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969092,0.0014918274,0.00013063443,0.00069818867,0.00047490204,0.00029528845],"domain_scores_gemma":[0.9932308,0.0026110096,0.00075357885,0.0013269237,0.0017660001,0.00031164827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005208607,0.0010644494,0.0010898873,0.0025968095,0.0010028387,0.0024062344,0.0011794863,0.00083189947,0.005627626],"category_scores_gemma":[0.016828544,0.0003794245,0.0015282161,0.0032844532,0.0017238451,0.0021477728,0.0018995517,0.0013771751,0.0012241552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016657167,0.00011520919,0.034807272,0.0002552661,0.0007757432,0.00054423284,0.000555778,0.20799094,0.00255838,0.54088026,0.017290404,0.19405983],"study_design_scores_gemma":[0.000016067866,0.000090436544,0.0072640507,0.00006585733,0.00009449473,0.0002969245,0.00018016974,0.8219389,0.0011991658,0.15165864,0.017096099,0.00009920044],"about_ca_topic_score_codex":0.009799151,"about_ca_topic_score_gemma":0.008895003,"teacher_disagreement_score":0.009799151,"about_ca_system_score_codex":0.0013275158,"about_ca_system_score_gemma":0.0028957108,"threshold_uncertainty_score":0.027546108},"labels":[],"label_agreement":null},{"id":"W4376271032","doi":"10.1093/jrsssc/qlad034","title":"The impact of directly observed therapy on the efficacy of Tuberculosis treatment: a Bayesian multilevel approach","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Propensity score matching; Covariate; Confounding; Multilevel model; Econometrics; Bayesian probability; Random effects model; Causal inference; Outcome (game theory); Statistics; Psychology; Mathematics; Medicine; Meta-analysis; Internal medicine","score_opus":0.12054633364644328,"score_gpt":0.3744787153095136,"score_spread":0.2539323816630703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376271032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033446133,0.0016378646,0.95585346,0.0047280053,0.00011493146,0.00021872814,0.0005986553,0.00015076452,0.0032514667],"genre_scores_gemma":[0.7114818,0.0021149686,0.28001145,0.0012103675,0.0005282903,0.00081418705,0.000477758,0.00011178307,0.003249349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96296793,0.03038077,0.0008662838,0.0024977326,0.0024074698,0.00087984407],"domain_scores_gemma":[0.8640771,0.120693915,0.006701138,0.005124117,0.0023552063,0.0010485462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04262423,0.0007398056,0.0026928035,0.0020520927,0.00090435584,0.0025301762,0.0033944678,0.002892945,0.009324678],"category_scores_gemma":[0.12580861,0.001177104,0.0037963362,0.0017195139,0.0025622572,0.0023892156,0.004963599,0.0043581137,0.00057039835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008915195,0.00036883235,0.02980103,0.00087112485,0.0032074216,0.00056268193,0.000860884,0.21680883,0.001553351,0.60167044,0.0033719845,0.14003186],"study_design_scores_gemma":[0.0002815916,0.00028712442,0.009263365,0.000406348,0.0013619023,0.00014625689,0.00008706512,0.60814375,0.0007460869,0.3753068,0.0038861143,0.00008356817],"about_ca_topic_score_codex":0.00877054,"about_ca_topic_score_gemma":0.007664471,"teacher_disagreement_score":0.04262423,"about_ca_system_score_codex":0.0023288545,"about_ca_system_score_gemma":0.002528528,"threshold_uncertainty_score":0.22542119},"labels":[],"label_agreement":null},{"id":"W4384834058","doi":"10.1093/jrsssc/qlad064","title":"A Tweedie Markov process and its application in fisheries stock assessment","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Autoregressive model; Markov chain; Applied mathematics; Econometrics; Autocorrelation; Computer science; Mathematics; Statistics","score_opus":0.014285609518547317,"score_gpt":0.2886606889203223,"score_spread":0.274375079401775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384834058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014485373,0.00021863123,0.9843523,0.0002250537,0.000034167144,0.00003867076,0.00006700398,0.00010192995,0.000476866],"genre_scores_gemma":[0.6020621,0.0009897734,0.3906361,0.00020390174,0.00013357215,0.0003146332,0.000454421,0.00011500493,0.0050905584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983381,0.00071629084,0.00008754269,0.0004139302,0.0003424411,0.0001016511],"domain_scores_gemma":[0.9871072,0.010131856,0.00096342864,0.00061775587,0.0009160137,0.0002638458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058890334,0.00063923287,0.0009696725,0.00240467,0.0008977652,0.0017673824,0.001968657,0.0019463513,0.0038533607],"category_scores_gemma":[0.02076789,0.00066222675,0.0011466979,0.0020376937,0.0017446695,0.0026297297,0.0018835705,0.002688255,0.0005910536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014274787,0.00009425533,0.006444295,0.00012565563,0.00008459104,0.00037757115,0.0002658812,0.7177931,0.0022519103,0.19649205,0.0011400041,0.0747879],"study_design_scores_gemma":[0.0000057872476,0.000017302296,0.00032359906,0.00001419075,0.000009195316,0.000046374982,0.000013409968,0.97136974,0.00033722885,0.027218744,0.00062388653,0.000020537915],"about_ca_topic_score_codex":0.008809227,"about_ca_topic_score_gemma":0.005050333,"teacher_disagreement_score":0.008809227,"about_ca_system_score_codex":0.0013338183,"about_ca_system_score_gemma":0.0010546815,"threshold_uncertainty_score":0.03114456},"labels":[],"label_agreement":null},{"id":"W4388454889","doi":"10.1093/jrsssc/qlad096","title":"Variable selection for individualised treatment rules with discrete outcomes","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Observational study; Feature selection; Variable (mathematics); Selection (genetic algorithm); Computer science; Machine learning; Data mining; Mathematics; Statistics","score_opus":0.05987198688175837,"score_gpt":0.3608529226673868,"score_spread":0.3009809357856284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388454889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006632159,0.00029666862,0.99179274,0.00023431296,0.000055872777,0.00027956083,0.00014339924,0.00020629828,0.00035897075],"genre_scores_gemma":[0.19284433,0.0005040609,0.80115974,0.0004729924,0.00025064105,0.0021507775,0.0010352061,0.00013665926,0.0014455736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95467454,0.036797777,0.0016975072,0.003889713,0.0023847588,0.00055555045],"domain_scores_gemma":[0.79728234,0.18702613,0.005169775,0.0063831075,0.003329242,0.0008093095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06382431,0.0011735265,0.0038575816,0.0028714752,0.0008505742,0.0027073633,0.0039048917,0.0023307598,0.006573423],"category_scores_gemma":[0.15633997,0.0010794217,0.0024282928,0.0023735003,0.002502131,0.0021653138,0.002462085,0.005009947,0.0010801624],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017512739,0.00045924657,0.010130899,0.0008604266,0.0013716429,0.00045703386,0.0006923167,0.3924713,0.001050281,0.20033279,0.0057922234,0.3846306],"study_design_scores_gemma":[0.0003742693,0.00019426967,0.00089029025,0.00012469548,0.000122654,0.00007543677,0.000036872778,0.81756485,0.00058217824,0.1779109,0.0020858042,0.000037686583],"about_ca_topic_score_codex":0.0021939613,"about_ca_topic_score_gemma":0.0017662713,"teacher_disagreement_score":0.06382431,"about_ca_system_score_codex":0.0011664974,"about_ca_system_score_gemma":0.0022478302,"threshold_uncertainty_score":0.33753926},"labels":[],"label_agreement":null},{"id":"W4388622580","doi":"10.1093/jrsssc/qlad100","title":"A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto; Queen's University","funders":"Institute of Circulatory and Respiratory Health; Natural Sciences and Engineering Research Council of Canada; AstraZeneca Canada; Canadian Institutes of Health Research; Canadian Lung Association; Canadian Allergy, Asthma and Immunology Foundation; AstraZeneca","keywords":"Cluster analysis; Longitudinal data; Computer science; Bayesian probability; Data set; Latent class model; Data mining; Longitudinal study; Set (abstract data type); Class (philosophy); Statistics; Artificial intelligence; Machine learning; Mathematics","score_opus":0.03344844872546207,"score_gpt":0.30899599874585554,"score_spread":0.27554755002039344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388622580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020375544,0.0005064747,0.97622037,0.0011133405,0.000065772874,0.0001630744,0.00044152408,0.0001994024,0.00091447623],"genre_scores_gemma":[0.4650753,0.0013547133,0.52274483,0.00041620026,0.00030432647,0.0014650256,0.001913333,0.00026422698,0.0064619463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946502,0.003952367,0.00012817168,0.00058047555,0.00047389552,0.00021484829],"domain_scores_gemma":[0.9758548,0.019814974,0.0011604491,0.0011235377,0.0015258952,0.00052033545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028868701,0.00081852224,0.0016749192,0.0020289728,0.0014311335,0.002653813,0.0043685394,0.0025449297,0.004383094],"category_scores_gemma":[0.03879801,0.0008694813,0.0021807472,0.0034572955,0.0016073419,0.0026627937,0.0028047052,0.0033278924,0.0008185324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043629983,0.00025840875,0.016403664,0.00020902525,0.0005766423,0.00049909996,0.0012175591,0.5311968,0.00057398464,0.36590862,0.00817966,0.07454031],"study_design_scores_gemma":[0.00004574182,0.00003520219,0.0011291467,0.00003557946,0.000051606916,0.00005559275,0.000071838076,0.9284048,0.00005606982,0.06843125,0.0016501391,0.000033046825],"about_ca_topic_score_codex":0.035856266,"about_ca_topic_score_gemma":0.027280862,"teacher_disagreement_score":0.035856266,"about_ca_system_score_codex":0.002614598,"about_ca_system_score_gemma":0.0027920378,"threshold_uncertainty_score":0.15267414},"labels":[],"label_agreement":null},{"id":"W4389719908","doi":"10.1093/jrsssc/qlad104","title":"Spatial modelling of infectious diseases with covariate measurement error","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; University of Manitoba","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Statistics; Inference; Observational error; Econometrics; Computer science; Population; Infectious disease (medical specialty); Spatial epidemiology; Mathematics; Medicine; Disease; Artificial intelligence; Environmental health; Epidemiology","score_opus":0.030876745663166616,"score_gpt":0.2571265089763711,"score_spread":0.2262497633132045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389719908","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034668077,0.0003745625,0.9633885,0.00050359365,0.00005886829,0.000055218265,0.0003433356,0.00022176311,0.00038605733],"genre_scores_gemma":[0.801855,0.00051509764,0.19387807,0.00021846953,0.00011543803,0.00027241025,0.0007119406,0.000075178876,0.0023582517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991911,0.0057475213,0.0003434634,0.0011985599,0.0004962956,0.00030319774],"domain_scores_gemma":[0.9656546,0.027372716,0.0030789205,0.0018123063,0.0017435962,0.00033787245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014686753,0.0009483331,0.0017948458,0.0017840483,0.00060100364,0.00151337,0.003301895,0.0020864992,0.0013682441],"category_scores_gemma":[0.04083703,0.0009400733,0.002000855,0.0025654584,0.002022215,0.0015314493,0.002580093,0.0018845495,0.0002330203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060151517,0.00002896361,0.011383612,0.00007926072,0.00014925413,0.00012283763,0.000114560076,0.94187486,0.00024948068,0.03538487,0.00038331343,0.010168814],"study_design_scores_gemma":[0.000012095314,0.000026228583,0.0009814642,0.000019388737,0.000019787896,0.000021045953,0.000014272165,0.9826303,0.0001133017,0.015615323,0.00053462386,0.000012083761],"about_ca_topic_score_codex":0.021271601,"about_ca_topic_score_gemma":0.011221898,"teacher_disagreement_score":0.021271601,"about_ca_system_score_codex":0.0018770117,"about_ca_system_score_gemma":0.0017351708,"threshold_uncertainty_score":0.077671885},"labels":[],"label_agreement":null},{"id":"W4391780126","doi":"10.1093/jrsssc/qlae008","title":"Revisiting the effects of maternal education on adolescents’ academic performance: Doubly robust estimation in a network-based observational study","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Fonds de recherche du Québec; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; National Institute on Aging; University of North Carolina at Chapel Hill","keywords":"Observational study; Estimation; Psychology; Statistics; Mathematics; Economics","score_opus":0.06762390301399934,"score_gpt":0.3678736315680582,"score_spread":0.30024972855405885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391780126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18165776,0.001675955,0.81077987,0.002312618,0.00015472194,0.00028350277,0.0011628679,0.00020828593,0.0017644989],"genre_scores_gemma":[0.86939836,0.00096966507,0.12606753,0.0005334982,0.00020512084,0.00055072614,0.0008163288,0.000052253137,0.0014064863],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97566074,0.019480297,0.00091022905,0.00238744,0.0011794572,0.00038179406],"domain_scores_gemma":[0.82451975,0.14854681,0.011594266,0.01294042,0.0018023794,0.0005963708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044618405,0.00067466096,0.0015015812,0.0013698941,0.00065198774,0.0018105308,0.003082364,0.0018764953,0.0021513493],"category_scores_gemma":[0.15397683,0.000667373,0.0019169216,0.0014183107,0.0019803143,0.0017879665,0.0024921705,0.0025069248,0.0002558371],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009031104,0.00061586633,0.41103405,0.0010771134,0.0055186427,0.0012226643,0.002841472,0.12970018,0.0018280166,0.29120693,0.0034596506,0.15059224],"study_design_scores_gemma":[0.00024372512,0.000749389,0.076520145,0.0004552019,0.0019773939,0.00041423726,0.0006479001,0.65697503,0.00173717,0.25190976,0.008253273,0.0001168382],"about_ca_topic_score_codex":0.013718003,"about_ca_topic_score_gemma":0.006636682,"teacher_disagreement_score":0.044618405,"about_ca_system_score_codex":0.0008530765,"about_ca_system_score_gemma":0.001455338,"threshold_uncertainty_score":0.23596752},"labels":[],"label_agreement":null},{"id":"W4393236302","doi":"10.1093/jrsssc/qlae017","title":"Estimating the timing of stillbirths in countries worldwide using a Bayesian hierarchical penalized splines regression model","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Alliance de recherche numérique du Canada; UNICEF","keywords":"Bayesian probability; Econometrics; Regression; Regression analysis; Statistics; Bayesian hierarchical modeling; Bayesian inference; Computer science; Mathematics","score_opus":0.017294262673060005,"score_gpt":0.31382572224361466,"score_spread":0.29653145957055466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393236302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38250872,0.0007339243,0.6106155,0.0013276096,0.0000848011,0.00014982714,0.0022059372,0.00043018878,0.0019435792],"genre_scores_gemma":[0.91733754,0.0005166885,0.07619934,0.00010118522,0.00005243661,0.0001965862,0.0030371943,0.000058981088,0.0024999187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978125,0.0013806055,0.00009718837,0.00036890391,0.0001520139,0.00018874554],"domain_scores_gemma":[0.9929261,0.004631809,0.0011960588,0.00041701013,0.00062448584,0.00020444793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075896583,0.0005389811,0.0011333913,0.0020212259,0.0003715905,0.00110601,0.0020311303,0.0010790295,0.0030720367],"category_scores_gemma":[0.020126022,0.0005571773,0.0014764793,0.002799747,0.00057353405,0.000901744,0.0013479765,0.0015315192,0.00047492108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026530263,0.00012216541,0.1321356,0.00013695337,0.00047819573,0.00027781966,0.00029220845,0.7744039,0.00061232137,0.025909215,0.0026954736,0.06267091],"study_design_scores_gemma":[0.000026427706,0.0000900342,0.01422901,0.00005241116,0.000062268235,0.000056533354,0.00010847186,0.9743825,0.0001238197,0.00976332,0.0010789302,0.000026241554],"about_ca_topic_score_codex":0.04496074,"about_ca_topic_score_gemma":0.025927145,"teacher_disagreement_score":0.04496074,"about_ca_system_score_codex":0.00065932743,"about_ca_system_score_gemma":0.0014577376,"threshold_uncertainty_score":0.089398086},"labels":[],"label_agreement":null},{"id":"W4396967793","doi":"10.1093/jrsssc/qlae020","title":"Testing for distributional structural change with unknown breaks: application to pricing crop insurance contracts","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Crop insurance; Actuarial science; Business; Economics; Econometrics; Financial economics; Geography; Agriculture","score_opus":0.011766432539399005,"score_gpt":0.23513823008657275,"score_spread":0.22337179754717373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396967793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7827566,0.0002089426,0.2138942,0.0011492232,0.000047149544,0.00016429019,0.00024592798,0.00025410988,0.0012794855],"genre_scores_gemma":[0.9841859,0.000030700037,0.015364035,0.00005414196,0.000021867445,0.000029987812,0.00009361192,0.000010639346,0.00020919091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99107414,0.006270525,0.0003329071,0.0011000418,0.0008619222,0.00036045545],"domain_scores_gemma":[0.624003,0.35399434,0.010732064,0.006561981,0.002461919,0.0022467875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0268628,0.00051905296,0.0013380686,0.0015312298,0.00079908926,0.0013641508,0.0018325939,0.0018590258,0.0027959743],"category_scores_gemma":[0.14740889,0.0003732663,0.0011759778,0.0014603192,0.0030469715,0.0026513052,0.0019362182,0.0034383785,0.00018158216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013592334,0.0011668795,0.24929996,0.00020010862,0.00080943777,0.000932651,0.0007694485,0.5895746,0.0033712536,0.0530746,0.0017564761,0.09768534],"study_design_scores_gemma":[0.00006797916,0.000381358,0.010546124,0.000011049759,0.000025334746,0.00007037886,0.000107204374,0.96980315,0.00055265956,0.01827607,0.00013685509,0.000021813565],"about_ca_topic_score_codex":0.0062784976,"about_ca_topic_score_gemma":0.004038586,"teacher_disagreement_score":0.0268628,"about_ca_system_score_codex":0.0014094563,"about_ca_system_score_gemma":0.0012935601,"threshold_uncertainty_score":0.14206576},"labels":[],"label_agreement":null},{"id":"W4399600420","doi":"10.1093/jrsssc/qlae028","title":"Two-phase biomarker studies for disease progression with multiple registries","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sampling design; Sampling (signal processing); Inverse probability; Inverse probability weighting; Covariate; Pooling; Missing data; Stratified sampling; Statistics; Weighting; Simple random sample; Computer science; Mathematics; Bayesian probability; Artificial intelligence; Medicine; Posterior probability; Population","score_opus":0.2747011443187014,"score_gpt":0.5343761593589086,"score_spread":0.2596750150402072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399600420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085024655,0.0011878415,0.90600777,0.0012531441,0.0004084668,0.0043656514,0.00027607294,0.00012295302,0.0013535499],"genre_scores_gemma":[0.596511,0.0005006843,0.3923074,0.0006348974,0.00019296179,0.0085237725,0.00021335592,0.000031795076,0.0010840854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8344771,0.15392399,0.0021032684,0.0048152907,0.0036406126,0.0010397309],"domain_scores_gemma":[0.85115045,0.112636656,0.0134195695,0.016251862,0.0046673426,0.0018740438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16269366,0.001423961,0.0032556015,0.0012967088,0.00070512324,0.0022437049,0.003173728,0.0030606524,0.004109619],"category_scores_gemma":[0.16296932,0.0012178795,0.0036784057,0.0017839201,0.00229151,0.002612819,0.0031105767,0.003083025,0.00045471647],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.027550023,0.0039292667,0.036726877,0.0026792742,0.0048914086,0.0012153232,0.0010330922,0.32191792,0.005818877,0.39273193,0.002968049,0.19853795],"study_design_scores_gemma":[0.014123724,0.021333002,0.006802601,0.0005182018,0.0023000888,0.0005700567,0.0002888323,0.62779886,0.00433823,0.3127003,0.008963788,0.00026232752],"about_ca_topic_score_codex":0.00046504257,"about_ca_topic_score_gemma":0.00037360645,"teacher_disagreement_score":0.16269366,"about_ca_system_score_codex":0.0013994117,"about_ca_system_score_gemma":0.0033864619,"threshold_uncertainty_score":0.86041665},"labels":[],"label_agreement":null},{"id":"W4403379352","doi":"10.1093/jrsssc/qlae053","title":"Modelling particle number size distribution: a continuous approach","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"Medical Research Council; Lancaster University","keywords":"Particle-size distribution; Statistical physics; Distribution (mathematics); Mathematics; Particle size; Computer science; Physics; Mathematical analysis; Engineering; Chemical engineering","score_opus":0.01937888016501419,"score_gpt":0.26855722601595133,"score_spread":0.24917834585093715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403379352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07394373,0.0004889299,0.9227693,0.00040948627,0.00007070179,0.000058786034,0.0006617464,0.00036956466,0.0012276882],"genre_scores_gemma":[0.9029324,0.00047156168,0.09267772,0.0001153041,0.00017536046,0.00015693161,0.0008871881,0.000094785806,0.0024886539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999046,0.0003926754,0.00003773271,0.00026571646,0.00016774196,0.00009015201],"domain_scores_gemma":[0.99446976,0.004182362,0.00050582085,0.00036410076,0.0003517798,0.00012622679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002725589,0.0008381527,0.00074444315,0.001491579,0.00034755116,0.0013332303,0.00186373,0.0017124702,0.0015478537],"category_scores_gemma":[0.0075133964,0.0005201899,0.0013287764,0.0011388665,0.0010089326,0.0012288416,0.0009610091,0.0013237826,0.00033411515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003847785,0.00003958196,0.006331737,0.000046642595,0.000058426813,0.00007290012,0.000037196754,0.97663236,0.0007568231,0.008545106,0.0003498869,0.0070907795],"study_design_scores_gemma":[0.0000023183168,0.000007860376,0.00048201735,0.0000028522745,0.0000036850997,0.0000078293215,0.0000038091725,0.99704176,0.00007798467,0.0021899235,0.00017578994,0.0000041010717],"about_ca_topic_score_codex":0.017501274,"about_ca_topic_score_gemma":0.005790617,"teacher_disagreement_score":0.017501274,"about_ca_system_score_codex":0.0009854785,"about_ca_system_score_gemma":0.0008359652,"threshold_uncertainty_score":0.0347988},"labels":[],"label_agreement":null},{"id":"W4404435360","doi":"10.1093/jrsssc/qlae058","title":"Bayesian optimization for personalized dose-finding trials with combination therapies","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada","keywords":"Bayesian probability; Personalized medicine; Computer science; Bayesian optimization; Medicine; Medical physics; Artificial intelligence; Bioinformatics; Biology","score_opus":0.24858919498272727,"score_gpt":0.47452656675349447,"score_spread":0.2259373717707672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404435360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01073196,0.0011099986,0.9850192,0.00087016285,0.000039415696,0.00039997805,0.00011518652,0.00016845173,0.0015456394],"genre_scores_gemma":[0.44740686,0.0015064153,0.5444999,0.00083571416,0.00013188166,0.0021389641,0.00032325694,0.0001915382,0.0029655027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9827466,0.014818261,0.00039240412,0.0007606061,0.0010046973,0.00027733846],"domain_scores_gemma":[0.96408993,0.03197375,0.0020068546,0.00087373215,0.00067713845,0.00037856057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027334189,0.0013149024,0.0035890876,0.0012308657,0.00035446187,0.0019298263,0.001698571,0.0017534607,0.00289589],"category_scores_gemma":[0.047644526,0.0015792277,0.0017613373,0.0011548954,0.0015965474,0.0017128395,0.0019227415,0.0030060501,0.00050639117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005900096,0.00009296838,0.000655569,0.00029617734,0.00028376462,0.00007778155,0.00006509417,0.92705005,0.00067663356,0.03481496,0.0011414213,0.03425547],"study_design_scores_gemma":[0.00020444278,0.00024932285,0.00034984192,0.000077982455,0.00010211144,0.000038802285,0.000013212191,0.9468286,0.0005632438,0.05007895,0.0014684484,0.000024916466],"about_ca_topic_score_codex":0.0018180255,"about_ca_topic_score_gemma":0.0016250699,"teacher_disagreement_score":0.027334189,"about_ca_system_score_codex":0.0020243207,"about_ca_system_score_gemma":0.003215026,"threshold_uncertainty_score":0.14455873},"labels":[],"label_agreement":null},{"id":"W4404709288","doi":"10.1093/jrsssc/qlae061","title":"Inferring bivariate associations with continuous data from studies using respondent-driven sampling","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"HIV, Drug Use, Sexual Risk","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences","keywords":"Homophily; Bivariate analysis; Statistics; Econometrics; Categorical variable; Inference; Causal inference; Respondent; Sampling (signal processing); Resampling; Statistical inference; Mathematics; Computer science; Psychology; Social psychology; Artificial intelligence","score_opus":0.13780761286894982,"score_gpt":0.40407028418233437,"score_spread":0.26626267131338455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404709288","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18954073,0.0020041869,0.8013313,0.0014321823,0.00022863182,0.001020365,0.0029807426,0.0002832125,0.0011788041],"genre_scores_gemma":[0.8001005,0.0007776456,0.1940196,0.00057491974,0.00014980079,0.0018883566,0.0020617638,0.000035007673,0.00039240567],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8570266,0.124500714,0.0075690188,0.0068220864,0.0035212503,0.0005603355],"domain_scores_gemma":[0.5642071,0.3724633,0.02585389,0.032090627,0.0046143,0.0007708829],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12650697,0.0006200336,0.0012306753,0.0053360006,0.0006161163,0.0025551273,0.0018939234,0.0016612017,0.0022334314],"category_scores_gemma":[0.3570214,0.00069380674,0.0030876203,0.0072352085,0.0018256935,0.0017034839,0.0029540043,0.0018756917,0.00040510215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069252006,0.00019987514,0.7945597,0.0029153544,0.007779036,0.00083589094,0.0023366404,0.032076836,0.0019840843,0.050276283,0.0032431667,0.1031006],"study_design_scores_gemma":[0.0006694169,0.0017840753,0.23762244,0.002042873,0.0066439155,0.0019758951,0.0023469506,0.41543153,0.004666082,0.30547252,0.021110281,0.00023395255],"about_ca_topic_score_codex":0.0027426565,"about_ca_topic_score_gemma":0.001811328,"teacher_disagreement_score":0.873493,"about_ca_system_score_codex":0.0006751617,"about_ca_system_score_gemma":0.0011566371,"threshold_uncertainty_score":0.6690408},"labels":[],"label_agreement":null},{"id":"W4404942304","doi":"10.1093/jrsssc/qlae070","title":"Personalized dynamic super learning: an application in predicting hemodiafiltration convection volumes","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; McGill University; Centre Hospitalier de l’Université de Montréal; Université de Montréal","funders":"","keywords":"Convection; Computer science; Mechanics; Physics","score_opus":0.006096610400868859,"score_gpt":0.2561080228183819,"score_spread":0.25001141241751307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404942304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37380517,0.0031890653,0.60602295,0.0045038736,0.00039471776,0.00025910253,0.0016054409,0.0055318316,0.004687765],"genre_scores_gemma":[0.9239062,0.00034321495,0.07237924,0.00064403616,0.00015530673,0.00011169161,0.0006554028,0.00013941187,0.0016654838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859697,0.000807998,0.000048928836,0.00026068505,0.00021080079,0.000074541],"domain_scores_gemma":[0.99037606,0.0071674655,0.0004442008,0.00088343583,0.0007587268,0.0003701322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066599473,0.0009198902,0.0012385071,0.00089782005,0.00038865925,0.0010041522,0.0014568863,0.0011450105,0.0023587202],"category_scores_gemma":[0.013459567,0.0003086266,0.00059183105,0.0008119313,0.0007383188,0.001180869,0.0017381377,0.002276892,0.0005149237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012602228,0.000542013,0.03005655,0.00019175562,0.00024082506,0.00040878245,0.0001578371,0.74760187,0.0032657066,0.0049832365,0.009023459,0.2022677],"study_design_scores_gemma":[0.000020469339,0.00010497172,0.00079929363,0.000008025852,0.000010423792,0.000037913305,0.000015109462,0.9936202,0.001037079,0.0038274382,0.0005062093,0.000012814187],"about_ca_topic_score_codex":0.0034904426,"about_ca_topic_score_gemma":0.002835261,"teacher_disagreement_score":0.0066599473,"about_ca_system_score_codex":0.0007235569,"about_ca_system_score_gemma":0.0014221403,"threshold_uncertainty_score":0.035221577},"labels":[],"label_agreement":null},{"id":"W4405336086","doi":"10.1093/jrsssc/qlae081","title":"Anthony C. Davison and Raphaël de Fondeville’s contribution to the Discussion of ‘Inference for extreme spatial temperature events in a changing climate with application to Ireland’ by Healy et al.","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Center for Research and Development in Mathematics and Applications; Science Foundation Ireland; European Commission","keywords":"Inference; Climate change; Climatology; Geography; Epistemology; Philosophy; Geology; Oceanography","score_opus":0.006767025693866065,"score_gpt":0.2600067529901347,"score_spread":0.25323972729626865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405336086","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018282812,0.05455527,0.17966339,0.6786732,0.06904995,0.00012667461,0.0012254674,0.00033367585,0.0145440735],"genre_scores_gemma":[0.10210777,0.04288994,0.1667163,0.47561365,0.16402952,0.00058051554,0.0012424823,0.001770414,0.04504937],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9891691,0.006323537,0.0004911396,0.0015167076,0.0021395064,0.00036007512],"domain_scores_gemma":[0.9322078,0.058408663,0.0014634243,0.0025433498,0.004232176,0.0011445914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012914265,0.0016706581,0.0017094194,0.002451676,0.0019210239,0.005464447,0.004847604,0.0083325505,0.011163225],"category_scores_gemma":[0.10274803,0.0011448457,0.0026249578,0.003269722,0.005244242,0.0068117594,0.004035863,0.014487333,0.0045364224],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064013024,0.000059406546,0.002408092,0.0005979322,0.00026439482,0.0004338968,0.0010537887,0.004219026,0.00022617281,0.16148406,0.78180397,0.047385223],"study_design_scores_gemma":[0.00003731148,0.000021034295,0.0012811956,0.00062822574,0.00004338432,0.00041293667,0.00030461882,0.0055524274,0.00023457434,0.30238104,0.6889401,0.00016303622],"about_ca_topic_score_codex":0.020097954,"about_ca_topic_score_gemma":0.018896569,"teacher_disagreement_score":0.020097954,"about_ca_system_score_codex":0.0033282863,"about_ca_system_score_gemma":0.002128738,"threshold_uncertainty_score":0.06829798},"labels":[],"label_agreement":null},{"id":"W4405448523","doi":"10.1093/jrsssc/qlae082","title":"Alexa A. Sochaniwsky and Paul D. McNicholas’s contribution to the Discussion of ‘Inference for extreme spatial temperature events in a changing climate with application to Ireland’ by Healy et al.","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Horizon 2020 Framework Programme; Center for Research and Development in Mathematics and Applications; Science Foundation Ireland; European Commission","keywords":"Inference; Climate change; Geography; Sociology; Epistemology; Philosophy; Geology; Oceanography","score_opus":0.0035610502754129456,"score_gpt":0.22709799029691738,"score_spread":0.22353694002150443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405448523","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014029427,0.018930925,0.039335907,0.883003,0.04517562,0.000042839943,0.00063563045,0.00020594476,0.011267335],"genre_scores_gemma":[0.117715105,0.039166067,0.04516031,0.62280107,0.10592894,0.00029877486,0.00089346257,0.0013502506,0.066686004],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9920672,0.0036887492,0.00042759255,0.00150178,0.0018765564,0.0004380243],"domain_scores_gemma":[0.9641792,0.025110226,0.0015334543,0.0015524753,0.006324248,0.0013003724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077837207,0.001153376,0.0010496046,0.001990959,0.0020104607,0.0056728125,0.0033488523,0.007787324,0.010321488],"category_scores_gemma":[0.081340365,0.00089732744,0.00168598,0.002290091,0.004203645,0.0088146245,0.003194069,0.012339761,0.005014063],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061078,0.00003017128,0.0026516088,0.0003821965,0.00010112095,0.00037050116,0.0014559808,0.0013880623,0.00016957101,0.09308003,0.86053014,0.03977956],"study_design_scores_gemma":[0.00002289165,0.000016891354,0.0013551711,0.00047591398,0.000027796648,0.0005516067,0.00087961217,0.0033622566,0.00029610962,0.12066138,0.87222373,0.00012668999],"about_ca_topic_score_codex":0.01491135,"about_ca_topic_score_gemma":0.018326009,"teacher_disagreement_score":0.01491135,"about_ca_system_score_codex":0.0030001383,"about_ca_system_score_gemma":0.0028586278,"threshold_uncertainty_score":0.041164756},"labels":[],"label_agreement":null},{"id":"W4405941653","doi":"10.1093/jrsssc/qlae073","title":"Wastewater surveillance using differentiable Gaussian processes","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Centre for Global Health Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Ecumenical Project for International Cooperation","keywords":"Differentiable function; Wastewater; Gaussian; Environmental science; Gaussian process; Computer science; Mathematics; Environmental engineering; Chemistry; Mathematical analysis","score_opus":0.00919682894247094,"score_gpt":0.22645893634278044,"score_spread":0.2172621074003095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405941653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053106736,0.00015222169,0.9449427,0.00029350288,0.0000185962,0.00002523881,0.00017024366,0.00021198747,0.0010787626],"genre_scores_gemma":[0.9334876,0.00023678542,0.062958784,0.0000943912,0.00004625604,0.000051256855,0.0003990021,0.00003807771,0.00268778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989466,0.00040018454,0.000043659304,0.00030730217,0.0001957951,0.00010650739],"domain_scores_gemma":[0.9965334,0.0022019425,0.00063235435,0.00020155322,0.0003483484,0.00008243197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027564596,0.00077794923,0.0008704666,0.0012905722,0.00030376733,0.0012289241,0.0015220504,0.0011803899,0.0010984103],"category_scores_gemma":[0.009704988,0.0005727286,0.0010864674,0.0010699659,0.0010099699,0.001625497,0.0010957984,0.0013161201,0.0002508087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005162407,0.000023762328,0.00414324,0.00002459559,0.000041173644,0.00006191155,0.000056905636,0.9557936,0.0008306355,0.022511007,0.0002793471,0.016182123],"study_design_scores_gemma":[0.0000025245852,0.000006446711,0.00035579075,0.0000023750958,0.0000028143083,0.000006380598,0.0000036581614,0.99529535,0.00010368103,0.004122381,0.00009396827,0.0000045780735],"about_ca_topic_score_codex":0.02770134,"about_ca_topic_score_gemma":0.016236601,"teacher_disagreement_score":0.02770134,"about_ca_system_score_codex":0.0016140153,"about_ca_system_score_gemma":0.001150409,"threshold_uncertainty_score":0.055080235},"labels":[],"label_agreement":null},{"id":"W4407567809","doi":"10.1093/jrsssc/qlaf009","title":"A Gaussian sliding windows regression model for hydrological inference","year":2025,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Canadian Statistical Sciences Institute; Alliance de recherche numérique du Canada; Norges Miljø- og Biovitenskapelige Universitet; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Streamflow; Computer science; Gaussian process; Sliding window protocol; Series (stratigraphy); Gaussian; Inference; Kriging; Regression; Parametrization (atmospheric modeling); Flow (mathematics); Algorithm; Artificial intelligence; Mathematics; Machine learning; Statistics; Geology; Window (computing); Geography","score_opus":0.013549247590098991,"score_gpt":0.2628518904145383,"score_spread":0.24930264282443934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407567809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019194292,0.00041557776,0.97846323,0.00026163287,0.000056142395,0.00003775665,0.00016657684,0.00042445466,0.0009803002],"genre_scores_gemma":[0.8385622,0.0010841511,0.14717163,0.00018793478,0.00018339438,0.000294524,0.00063306285,0.00022365281,0.011659482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99877745,0.00045253464,0.0000627282,0.00037354426,0.00019138766,0.00014243125],"domain_scores_gemma":[0.9969379,0.0021658416,0.00022299071,0.00016675021,0.00042819383,0.00007840905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004057439,0.0010139907,0.001403733,0.00084430724,0.00041931443,0.0012530223,0.0026374024,0.0016419815,0.004054122],"category_scores_gemma":[0.008347138,0.0006716942,0.0011971461,0.0012202745,0.0010160819,0.0019533928,0.0009736963,0.0022269916,0.0009505851],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093441755,0.000032788223,0.00066616596,0.000038125618,0.00004626977,0.00006689863,0.000048472226,0.94883674,0.00082033983,0.027539162,0.0005934361,0.021218179],"study_design_scores_gemma":[0.000002069455,0.00000512194,0.000040827203,0.0000018034277,0.0000037952477,0.0000024531503,0.0000013756701,0.9979128,0.00008003616,0.0018408705,0.000106410356,0.0000024812389],"about_ca_topic_score_codex":0.022824556,"about_ca_topic_score_gemma":0.011359283,"teacher_disagreement_score":0.022824556,"about_ca_system_score_codex":0.001284064,"about_ca_system_score_gemma":0.0013976265,"threshold_uncertainty_score":0.045383394},"labels":[],"label_agreement":null},{"id":"W4408755504","doi":"10.1093/jrsssc/qlaf021","title":"Bayesian inference for the Markov-modulated Poisson process with an outcome process","year":2025,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outcome (game theory); Inference; Bayesian inference; Poisson process; Process (computing); Computer science; Bayesian probability; Poisson distribution; Econometrics; Artificial intelligence; Statistics; Mathematics; Mathematical economics","score_opus":0.01172868295093676,"score_gpt":0.3128658715645225,"score_spread":0.30113718861358574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408755504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024822585,0.00049294316,0.9717234,0.0009748029,0.000074254065,0.00014049468,0.00043970672,0.00024085959,0.0010908819],"genre_scores_gemma":[0.6102298,0.0015363836,0.37621024,0.0006558976,0.00048328485,0.0009414875,0.002226574,0.00022271856,0.007493666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.994601,0.003588475,0.00018357263,0.00087357714,0.00047832876,0.00027515393],"domain_scores_gemma":[0.9501008,0.045185603,0.0016773057,0.0012713878,0.0012149219,0.0005500106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01932336,0.0011388025,0.0026355756,0.002873711,0.001090488,0.0022927588,0.0038865397,0.00284158,0.006654492],"category_scores_gemma":[0.06278374,0.0016888984,0.0026694022,0.0025644968,0.002805212,0.0035805185,0.0024247249,0.0045784167,0.0009245503],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003629149,0.0001758139,0.009890851,0.00024456315,0.00030688653,0.00030341494,0.00040900338,0.5294026,0.00047483016,0.41300368,0.0034598487,0.04196549],"study_design_scores_gemma":[0.000067468936,0.000026137242,0.0007912943,0.000047006357,0.000035027,0.000037347578,0.000027096648,0.870402,0.00012817293,0.12757696,0.0008323197,0.00002912624],"about_ca_topic_score_codex":0.02303131,"about_ca_topic_score_gemma":0.020681877,"teacher_disagreement_score":0.02303131,"about_ca_system_score_codex":0.0027974516,"about_ca_system_score_gemma":0.0030011954,"threshold_uncertainty_score":0.10219294},"labels":[],"label_agreement":null},{"id":"W4409560806","doi":"10.1093/jrsssc/qlaf030","title":"Investigating the effect of climate-related hazards on claim frequency prediction in motor insurance with incomplete data","year":2025,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Actuarial science; Econometrics; Environmental science; Business; Computer science; Economics","score_opus":0.01055893924341151,"score_gpt":0.22186842252275435,"score_spread":0.21130948327934285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409560806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9890638,0.0006002852,0.0067750346,0.0007432699,0.000019675872,0.000019708625,0.0022618612,0.00006462727,0.00045184814],"genre_scores_gemma":[0.9944476,0.00014057713,0.0017461587,0.000039980812,0.000030483372,0.0000099192175,0.003334651,0.000008558928,0.00024208725],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978173,0.0011191803,0.00014111065,0.0004886908,0.00020556244,0.00022823231],"domain_scores_gemma":[0.928608,0.060292657,0.006325445,0.0029081304,0.0009341949,0.0009315837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014115338,0.00043768348,0.0008022395,0.0016111456,0.0004161359,0.0013288246,0.0011542548,0.0013989327,0.0017098173],"category_scores_gemma":[0.038104225,0.00031406945,0.0010569219,0.0013011707,0.0007421991,0.0012258687,0.0011723472,0.0014681862,0.00030459443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069166673,0.00026567676,0.77973866,0.00014393104,0.00038439597,0.00036500348,0.00026747084,0.19579826,0.0005980216,0.0025232623,0.0019755345,0.017248074],"study_design_scores_gemma":[0.000036992435,0.00022091795,0.18288116,0.00008973192,0.00015388326,0.00013869365,0.0003036265,0.8112398,0.0005584181,0.0029377274,0.0014093709,0.000029700848],"about_ca_topic_score_codex":0.019078787,"about_ca_topic_score_gemma":0.014575974,"teacher_disagreement_score":0.019078787,"about_ca_system_score_codex":0.00091154873,"about_ca_system_score_gemma":0.000997093,"threshold_uncertainty_score":0.07464993},"labels":[],"label_agreement":null},{"id":"W4415261409","doi":"10.1093/jrsssc/qlaf052","title":"Gaussian process with dissolution spline kernel for in vitro dissolution testing","year":2025,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"European Regional Development Fund","keywords":"Dissolution; Kernel (algebra); Piecewise; Spline (mechanical); Gaussian process; Kernel principal component analysis; Parametric statistics; Gaussian","score_opus":0.010304921948276785,"score_gpt":0.2762404070129939,"score_spread":0.2659354850647171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415261409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026371717,0.00033668915,0.9716339,0.00029308887,0.00004961105,0.000070997216,0.00017775247,0.0003669818,0.0006993034],"genre_scores_gemma":[0.81864965,0.0007681422,0.1731968,0.00024759036,0.00009382865,0.00042016004,0.0006522633,0.00018182913,0.005789719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982815,0.00068966195,0.00008497939,0.0003096248,0.00047136203,0.00016292275],"domain_scores_gemma":[0.995033,0.0032543754,0.00050979824,0.00029219824,0.0008060239,0.0001045478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046505895,0.0009637393,0.0013085302,0.0012349407,0.00044271935,0.0010935819,0.0018614527,0.002456802,0.0016962651],"category_scores_gemma":[0.012270218,0.0004960182,0.0020004744,0.0014167764,0.0013033976,0.0012871467,0.0011814671,0.0027944283,0.0006687011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013430238,0.00007241353,0.0024289952,0.00009586558,0.000053352447,0.00017451105,0.00007563369,0.9492637,0.0026719319,0.021516263,0.00081892835,0.022694176],"study_design_scores_gemma":[0.0000041505746,0.000014505094,0.00016227864,0.0000031203483,0.0000048730667,0.000011301471,0.000002734179,0.99770594,0.00023881526,0.0016623681,0.0001841464,0.000005797361],"about_ca_topic_score_codex":0.0149003705,"about_ca_topic_score_gemma":0.006535007,"teacher_disagreement_score":0.0149003705,"about_ca_system_score_codex":0.0013442918,"about_ca_system_score_gemma":0.0014094682,"threshold_uncertainty_score":0.029627264},"labels":[],"label_agreement":null}]}