{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":431,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":431,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"e8d550d2262f","filters":{"topic":"Advanced Statistical Process Monitoring"}},"results":[{"id":"W2109802067","doi":"10.1093/biostatistics/1.4.441","title":"Monitoring surgical performance using risk-adjusted cumulative sum charts","year":2000,"lang":"en","type":"article","venue":"Biostatistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":473,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"CUSUM; Context (archaeology); Computer science; Quality (philosophy); Surgical procedures; Statistics; Variable (mathematics); Medicine; Surgery; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1950489529157844,"gpt":0.4331799175942861,"spread":0.2381309646785017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009734448,0.0003069532,0.0004409318,0.0001685926,0.000587786,0.0002299589,0.0005403832,0.0001236288,0.0009114675],"category_scores_gemma":[0.003030225,0.0002596203,0.00006804789,0.0008876569,0.0002611114,0.0005119849,0.0001026437,0.0004021311,0.0007091048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001437586,"about_ca_system_score_gemma":0.00007723223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006034223,"about_ca_topic_score_gemma":0.000002450477,"domain_scores_codex":[0.9958203,0.000225081,0.0009498041,0.0007124726,0.001656006,0.0006362683],"domain_scores_gemma":[0.9953519,0.002982126,0.0003467984,0.0005336165,0.0004752809,0.0003103526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001933465,0.00008299267,0.1246063,0.00002314697,0.00002615634,0.0001936078,0.0005925929,0.01633832,0.0001194232,0.0009097556,0.000235199,0.8566791],"study_design_scores_gemma":[0.002473486,0.0004197203,0.2656032,0.0003207403,0.0001466847,0.0001418749,0.0009835425,0.6486214,0.00582102,0.04089521,0.03280447,0.001768543],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.90031,0.0002770767,0.09464999,0.00003536553,0.001097739,0.0002291535,0.0003791697,0.0001298709,0.00289166],"genre_scores_gemma":[0.9211321,0.0002548294,0.07640991,0.000009731538,0.0005614497,0.000006100987,0.000006006022,0.00003623377,0.001583593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8549106,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137690751","doi":"10.1016/j.compchemeng.2004.01.009","title":"On-line outlier detection and data cleaning","year":2004,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":431,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Anomaly detection; Outlier; Line (geometry); Computer science; Data mining; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0930221500322236,"gpt":0.3712197622785883,"spread":0.2781976122463647,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000306102,0.0001417333,0.0001889542,0.00009184769,0.00005569925,0.000120392,0.000582889,0.00005463646,0.0000038138],"category_scores_gemma":[0.002746344,0.0001262579,0.00002051536,0.0002785444,0.0000395788,0.0002940867,0.0004320844,0.0002603657,0.00002813172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006580996,"about_ca_system_score_gemma":0.000009438836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002192359,"about_ca_topic_score_gemma":1.973174e-7,"domain_scores_codex":[0.9984361,0.000006401163,0.000301768,0.0005626142,0.0004600113,0.0002331129],"domain_scores_gemma":[0.9982817,0.0009547562,0.0000493864,0.0005058405,0.00004998426,0.0001583061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002784295,0.00003415571,0.00002154826,0.00002191366,0.00001559763,0.00002895224,0.0001407754,0.6488472,0.05304279,0.003393148,0.00007076264,0.2943554],"study_design_scores_gemma":[0.000569121,0.00005047923,0.0001200049,0.00008436772,0.000006993277,0.00002183919,0.00001976586,0.9445537,0.03344424,0.02012239,0.0007408084,0.000266272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09027544,0.00005893782,0.9089016,0.00008533298,0.0004623395,0.00004649851,0.000005264975,0.0001202049,0.00004433363],"genre_scores_gemma":[0.906108,0.000001446403,0.09359632,0.0000535741,0.0002128701,0.000001858357,0.000003255681,0.00001667231,0.0000059914],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8158326,"threshold_uncertainty_score":0.5148646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2092433731","doi":"10.1002/qre.538","title":"A Bibliography of Process Capability Papers","year":2003,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":153,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Library science; Smiley; History; Operations research; Engineering; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.06189703946502745,"gpt":0.4126821133224924,"spread":0.350785073857465,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002831156,0.0001249516,0.0002598579,0.001916441,0.00004915917,0.00005487476,0.0003248769,0.00007026752,0.0001581732],"category_scores_gemma":[0.01903571,0.0001008884,0.0000921137,0.003876667,0.000162084,0.0002591129,0.00004222503,0.000175172,0.000003393725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001233763,"about_ca_system_score_gemma":0.00001917856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002310434,"about_ca_topic_score_gemma":0.000001361599,"domain_scores_codex":[0.9974704,0.0001124922,0.000793237,0.0004423643,0.001016862,0.000164581],"domain_scores_gemma":[0.9967431,0.002093061,0.0001655784,0.0003250469,0.0005594254,0.0001138251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001270667,0.0004965296,0.7688305,0.0004783836,0.00008638814,0.000003013685,0.0007454153,0.07836398,0.004191476,0.1406695,0.0005325146,0.005475269],"study_design_scores_gemma":[0.0006594344,0.00008153659,0.6710668,0.00006035953,0.00001367218,0.000008138989,0.0007355832,0.009376484,0.004643823,0.2968465,0.01605933,0.0004483322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8662684,0.0001101001,0.1221612,0.0004451453,0.001059464,0.0001833733,0.00007841839,0.00007895859,0.009614908],"genre_scores_gemma":[0.9898089,0.0000906489,0.01000364,0.00002013547,0.000029811,0.00001209931,0.00000175724,0.00000651428,0.00002647168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.156177,"threshold_uncertainty_score":0.9892274,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017302001","doi":"10.1016/j.eswa.2010.11.044","title":"A new nonparametric EWMA Sign Control Chart","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":136,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"National Science Council","keywords":"EWMA chart; Control chart; Chart; Statistics; X-bar chart; Computer science; Control limits; Nonparametric statistics; Normality; Shewhart individuals control chart; Step detection; Mathematics; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.04499023868176891,"gpt":0.3833046998646136,"spread":0.3383144611828447,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000637851,0.0002023792,0.000370476,0.0002621925,0.0003247877,0.0003341584,0.0008630913,0.0001027793,0.0001638142],"category_scores_gemma":[0.001166734,0.0001383484,0.00004892304,0.001663309,0.0001062788,0.0003091386,0.00004180043,0.000304649,0.001401135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003840562,"about_ca_system_score_gemma":0.0001624798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001581538,"about_ca_topic_score_gemma":0.00002254197,"domain_scores_codex":[0.9970655,0.00005806278,0.0006336944,0.0006865928,0.001199879,0.0003562215],"domain_scores_gemma":[0.995447,0.002212216,0.0003004017,0.001109088,0.0004891857,0.0004421215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002057173,0.0004247365,0.01156037,0.00004160267,0.0001299988,0.00002411164,0.001736189,0.002734038,0.04169273,0.3663185,0.0902934,0.4848386],"study_design_scores_gemma":[0.001437128,0.0001216069,0.002172046,0.00002681067,0.00001778997,0.00007557736,0.001029643,0.008426374,0.0008496324,0.01516964,0.970091,0.0005827736],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004027708,0.000471042,0.991859,0.0005229079,0.0004687872,0.001133249,0.00002589761,0.0001560014,0.004960353],"genre_scores_gemma":[0.9383841,0.000003442168,0.054798,0.0001107819,0.001029647,0.001684409,0.000003217006,0.00003019442,0.003956224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9379813,"threshold_uncertainty_score":0.9993764,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2109875526","doi":"10.1093/intqhc/mzr082","title":"Assessing the effect of estimation error on risk-adjusted CUSUM chart performance","year":2011,"lang":"en","type":"article","venue":"International Journal for Quality in Health Care","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":116,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"CUSUM; Statistics; Control chart; Estimation; Chart; Computer science; Process (computing); Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.3363041345534744,"gpt":0.5949486759531024,"spread":0.2586445413996281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007950612,0.0001193571,0.0002893132,0.0002379526,0.000295298,0.0001241931,0.0007308392,0.00004902108,0.00003261793],"category_scores_gemma":[0.01389949,0.0000728355,0.0001008421,0.000214333,0.00007475587,0.000614591,0.00004881637,0.0004356717,0.00001214584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003689837,"about_ca_system_score_gemma":0.0001415258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001349619,"about_ca_topic_score_gemma":0.00003181323,"domain_scores_codex":[0.9958524,0.0007820828,0.001361482,0.0002222515,0.00155402,0.0002277269],"domain_scores_gemma":[0.9923271,0.005042292,0.001383298,0.0002010468,0.0009521946,0.00009407893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009252416,0.00005656587,0.2172384,0.0002176989,0.00002985893,0.000004431564,0.008515153,0.0144007,0.00001399179,0.00209415,0.0002036544,0.7563002],"study_design_scores_gemma":[0.002735931,0.001968074,0.8930613,0.001051255,0.00001670322,0.00003684348,0.01002219,0.0604196,0.003430468,0.02571751,0.001235417,0.0003046845],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8892173,0.0001684635,0.1055257,0.0009584696,0.003324592,0.0003743239,0.00008515862,0.00001344737,0.0003325706],"genre_scores_gemma":[0.9937149,0.00001995315,0.005883201,0.0001271838,0.0001905876,0.0000228462,0.000006277977,0.00000929418,0.00002576666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7559955,"threshold_uncertainty_score":0.9944069,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1993186055","doi":"10.1109/tase.2011.2176490","title":"Performance Assessment and Design for Univariate Alarm Systems Based on FAR, MAR, and AAD","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":116,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Univariate; ALARM; Computation; Constant false alarm rate; Computer science; False alarm; Reliability engineering; Real-time computing; Variable (mathematics); Engineering; Algorithm; Artificial intelligence; Machine learning; Multivariate statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1142831347647703,"gpt":0.3553357846621226,"spread":0.2410526498973523,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001759061,0.0001253164,0.0001461239,0.0003990401,0.0004077075,0.0002320953,0.0001499501,0.00003808899,0.000005107199],"category_scores_gemma":[0.0001452968,0.0001036448,0.00001282696,0.0005348158,0.000121339,0.000794281,0.000002483386,0.0001001304,0.000002477522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007146675,"about_ca_system_score_gemma":0.0000709656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005690813,"about_ca_topic_score_gemma":3.589538e-7,"domain_scores_codex":[0.9984814,0.0000219983,0.0002690358,0.000378018,0.0006343773,0.0002151852],"domain_scores_gemma":[0.998708,0.0007208881,0.00006649314,0.0001700397,0.0001965006,0.0001381497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004215196,0.00004257635,0.0001414944,0.00007715786,0.000006043704,0.000001253481,0.0004767982,0.8431898,0.004874495,0.001422406,0.000006209514,0.1497196],"study_design_scores_gemma":[0.0002602456,0.0001736119,0.009787955,0.00005068594,0.000007947357,0.000003973596,0.0001096917,0.9865546,0.002694344,0.0001797471,0.00005433751,0.0001228908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04445184,0.00001398148,0.9546107,0.0000343565,0.0004195169,0.0002643008,0.000008409268,0.00007981432,0.000117056],"genre_scores_gemma":[0.918978,0.00001984983,0.08087625,0.00002109009,0.000009821175,0.00005883428,1.334664e-7,0.000008231603,0.00002781787],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8745261,"threshold_uncertainty_score":0.4226512,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W88365156","doi":"10.1080/00224065.2004.11980277","title":"EWMA Charts for Monitoring the Mean of Censored Weibull Lifetimes","year":2004,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":111,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"EWMA chart; Statistics; Censoring (clinical trials); Weibull distribution; Control chart; Mathematics; Chart; X-bar chart; Moving average; Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.1705835624351209,"gpt":0.4908891633142227,"spread":0.3203056008791018,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003842289,0.0001294081,0.0005596254,0.0003758506,0.0001545443,0.0000376492,0.001211029,0.0001581167,0.0000109434],"category_scores_gemma":[0.01824984,0.00007809046,0.0001665527,0.0006819477,0.0004244853,0.0002248234,0.0001264751,0.0003995721,0.000007555849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000820717,"about_ca_system_score_gemma":0.0001127683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006624045,"about_ca_topic_score_gemma":0.000002920109,"domain_scores_codex":[0.9968074,0.0001054746,0.00158252,0.0002165816,0.00100311,0.0002849308],"domain_scores_gemma":[0.9941836,0.002414953,0.001546166,0.0004676596,0.001309918,0.00007771454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007804771,0.0004978674,0.02361443,0.0001165369,0.0002363236,0.00005322158,0.003230429,0.003647156,0.05725982,0.5407923,0.0007123427,0.3690591],"study_design_scores_gemma":[0.0008197535,0.0003476153,0.003617833,0.00007345431,0.00002108493,0.00004857798,0.003809993,0.00001922231,0.1161458,0.8717314,0.003259512,0.0001057832],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5227522,0.0009591564,0.4503466,0.02426177,0.001262539,0.0002229347,0.00002457858,0.00004477283,0.0001254152],"genre_scores_gemma":[0.933648,0.00003517419,0.06593608,0.00004000248,0.0002710919,0.000005310265,9.829865e-8,0.00001100166,0.00005324193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4108958,"threshold_uncertainty_score":0.9900199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2052951278","doi":"10.1111/j.0006-341x.2001.00598.x","title":"Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Mount Sinai Hospital; BC Cancer Agency; University of British Columbia","funders":"","keywords":"Bayes' theorem; Statistics; Odds; Computer science; Odds ratio; Control (management); Prior probability; Bayesian probability; Mathematics; Econometrics; Artificial intelligence; Logistic regression","retraction":null,"screen_n_in":null,"score":{"opus":0.1616375994058054,"gpt":0.4060722638635694,"spread":0.244434664457764,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001303859,0.0001257549,0.0003755425,0.002256123,0.0001110419,0.00008813011,0.0003246342,0.00006929144,0.0001008999],"category_scores_gemma":[0.007823113,0.00008480739,0.0000933921,0.02129021,0.0002051491,0.0002359785,0.00003518426,0.00007865311,0.00002835557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006228287,"about_ca_system_score_gemma":0.00006370639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001836405,"about_ca_topic_score_gemma":0.00003417116,"domain_scores_codex":[0.9977459,0.0001356972,0.0006486035,0.0004263015,0.0008129932,0.0002304981],"domain_scores_gemma":[0.9947774,0.003230917,0.000331798,0.000512151,0.00102242,0.0001253596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002425342,0.0004902093,0.6290369,0.00004575927,0.0003707643,0.0002053283,0.001079224,0.00454336,0.0006203052,0.004612167,0.0001449317,0.3586085],"study_design_scores_gemma":[0.007329765,0.003567802,0.6965138,0.00007395953,0.004210709,0.0008499441,0.01469319,0.149471,0.007209235,0.05040655,0.0631807,0.00249342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2620186,0.0005502031,0.7360422,0.00004586606,0.0001158154,0.0001505822,0.00006502319,0.00003018745,0.0009815018],"genre_scores_gemma":[0.9864686,0.00000900433,0.01282383,0.000004423877,0.00007811993,0.00001908783,0.00000261991,0.0000080321,0.0005862644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.72445,"threshold_uncertainty_score":0.9995127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2163077119","doi":"10.1002/sim.3788","title":"Risk‐adjusted survival time monitoring with an updating exponentially weighted moving average (EWMA) control chart","year":2009,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":99,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"EWMA chart; Control chart; Chart; Computer science; Statistics; Moving average; Set (abstract data type); Data mining; Medicine; Mathematics; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.04722158145783525,"gpt":0.3800120459529197,"spread":0.3327904644950844,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003844918,0.0004079371,0.0009370653,0.0004550037,0.0003593517,0.0001416219,0.0007854902,0.0001126611,0.0004199618],"category_scores_gemma":[0.01263731,0.0003021726,0.00002739339,0.001046847,0.0002938382,0.0005715609,0.00006001133,0.0007724555,0.00008139797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001525796,"about_ca_system_score_gemma":0.0000975917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001130878,"about_ca_topic_score_gemma":0.00005027819,"domain_scores_codex":[0.9934953,0.0005902422,0.001422091,0.0009483627,0.002789325,0.000754624],"domain_scores_gemma":[0.9922799,0.005163589,0.0006923999,0.0007158986,0.0007377599,0.0004103852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002719759,0.0006492079,0.1772604,0.00008555596,0.0001860589,0.002807984,0.006815806,0.01799612,0.01543186,0.03649199,0.001248049,0.7383072],"study_design_scores_gemma":[0.01173162,0.003494634,0.3543335,0.0008659879,0.0002313177,0.00004059615,0.004511135,0.3434962,0.0007487784,0.2787266,0.0003705584,0.001449077],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06300242,0.0001206375,0.9331825,0.0002499036,0.000831918,0.0003619047,0.0002096127,0.0001249771,0.001916075],"genre_scores_gemma":[0.8337959,0.00002604132,0.1649218,0.00007619913,0.0008254779,0.00001176571,0.00004145328,0.00003782285,0.0002635416],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7707934,"threshold_uncertainty_score":0.999943,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2087167202","doi":"10.1016/s0925-5273(99)00079-1","title":"Production and preventive maintenance rates control for a manufacturing system: An experimental design approach","year":2000,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":96,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Preventive maintenance; Computer science; Mathematical optimization; Production control; Formalism (music); Factorial experiment; Reliability engineering; Production (economics); Industrial engineering; Mathematics; Engineering; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.07662796045032892,"gpt":0.3743088354988119,"spread":0.297680875048483,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001963179,0.0001279684,0.000252256,0.0001876362,0.0001201136,0.0002252585,0.0003783646,0.0000380395,0.00002772368],"category_scores_gemma":[0.001148262,0.0001072216,0.00006611869,0.0000434705,0.00009221176,0.001760247,0.00001820279,0.0001184721,0.000005689969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002759632,"about_ca_system_score_gemma":0.00005354522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001906117,"about_ca_topic_score_gemma":7.011556e-7,"domain_scores_codex":[0.9981942,0.0001168136,0.0008142483,0.0004134173,0.0003164833,0.0001448785],"domain_scores_gemma":[0.9982361,0.0002206048,0.0006592835,0.000146942,0.0006474575,0.00008964258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006042408,0.0006521614,0.0008018241,0.00004615684,0.0004006741,0.00001155126,0.002067112,0.6642785,0.008674577,0.004582572,0.001380777,0.3110617],"study_design_scores_gemma":[0.007092841,0.001942624,0.006096022,0.0004378137,0.0001812042,0.004960438,0.01762502,0.1698613,0.6476333,0.1303712,0.01246786,0.001330461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4874467,0.0001511344,0.5085739,0.0005223091,0.002773608,0.0003972575,0.000016137,0.00001521038,0.0001037259],"genre_scores_gemma":[0.9422134,0.0000439255,0.05549312,0.0000200726,0.001587104,0.00002804008,0.00000161245,0.00001421917,0.0005985544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6389587,"threshold_uncertainty_score":0.4372369,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2018100203","doi":"10.1080/02664760120074951","title":"Economic design of X charts with variable parameters: The Markov chain approach","year":2001,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Control chart; Control limits; Markov chain; Statistics; Variable (mathematics); Sampling (signal processing); Statistical process control; Sample size determination; Mathematics; Exponential distribution; Interval (graph theory); Limit (mathematics); Computer science; Mathematical optimization; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.08370642413956754,"gpt":0.3382596461692194,"spread":0.2545532220296519,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00297255,0.0001793138,0.0005178378,0.0001593848,0.0001329009,0.0001315198,0.0007782918,0.00005487773,0.00009090981],"category_scores_gemma":[0.001013997,0.00009082697,0.00003709302,0.0003529763,0.000269903,0.0001819491,0.00006031173,0.0003317413,0.00001676406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008694852,"about_ca_system_score_gemma":0.0002286143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003361479,"about_ca_topic_score_gemma":0.000001161086,"domain_scores_codex":[0.9971337,0.0001146039,0.001152043,0.0002435704,0.001062975,0.0002931433],"domain_scores_gemma":[0.9926122,0.005118955,0.001361366,0.0003710291,0.0003905076,0.0001459839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003442043,0.000270827,0.0008032414,0.00004894461,0.0002262007,0.0001117171,0.00153893,0.6633593,0.0004086709,0.1713114,0.008765557,0.1497131],"study_design_scores_gemma":[0.00186369,0.001013205,0.0008549075,0.00005798696,0.0001636043,0.0003714621,0.003511464,0.1203596,0.0007029043,0.8659719,0.004697857,0.0004314632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001807222,0.0000390971,0.9947318,0.00005715,0.0001908045,0.0002096326,0.000069016,0.000005633472,0.002889583],"genre_scores_gemma":[0.2857299,0.00003460731,0.7139189,0.00004360963,0.0001129167,0.000006634415,0.000001060453,0.0000174873,0.0001349113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6946605,"threshold_uncertainty_score":0.3703817,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2086720844","doi":"10.1177/0272989x0102100301","title":"Risk-Adjusted Monitoring of Binary Surgical Outcomes","year":2001,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Chart; Surgical procedures; Medicine; CUSUM; Control chart; Computer science; Cumulative risk; Statistics; Surgery; Mathematics; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.1256849586185509,"gpt":0.4730050120026749,"spread":0.347320053384124,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00515402,0.0002824442,0.0008331148,0.000480763,0.0002809597,0.0001065857,0.001581918,0.0002715113,0.002026309],"category_scores_gemma":[0.08854188,0.0001907179,0.0002432351,0.001731953,0.0002989954,0.000392858,0.0006612876,0.0007077795,0.0003255804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007598808,"about_ca_system_score_gemma":0.00009946586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001338655,"about_ca_topic_score_gemma":0.000004479919,"domain_scores_codex":[0.9889849,0.0003602104,0.001903974,0.0008080237,0.007360792,0.0005820552],"domain_scores_gemma":[0.9710898,0.02639644,0.0006298661,0.0008515019,0.0005334985,0.0004989094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001237281,0.00006610244,0.4419551,0.00000363332,0.000009746433,0.00058366,0.00005281627,0.0004764222,0.00001660272,0.0004106265,0.0001620494,0.5561395],"study_design_scores_gemma":[0.001897876,0.0002138276,0.8355974,0.0006971282,0.0000365047,0.000160157,0.0008019065,0.0200102,0.000145702,0.1260994,0.01383479,0.0005051086],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6672402,0.0003892868,0.3286406,0.0001935862,0.001733656,0.0001115557,0.000009222751,0.00008782362,0.001594047],"genre_scores_gemma":[0.9558005,0.0001591454,0.04329891,0.00003244177,0.0003787725,0.000008695579,6.744755e-7,0.00002761722,0.0002932367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5556344,"threshold_uncertainty_score":0.998886,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2025168569","doi":"10.1016/j.jmva.2008.08.005","title":"Monitoring parameter change in<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si50.gif\" display=\"inline\" overflow=\"scroll\"><mml:mstyle mathvariant=\"normal\"><mml:mi>AR</mml:mi></mml:mstyle><mml:mrow><mml:mo>(</mml:mo><mml:mi>p</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math>time series models","year":2008,"lang":"lv","type":"article","venue":"Journal of Multivariate Analysis","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":83,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"CUSUM; Autoregressive model; Mathematics; Scroll; Series (stratigraphy); Algorithm; Statistics; Autoregressive–moving-average model; Applied mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04217043394014507,"gpt":0.2981876160440374,"spread":0.2560171821038923,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.004654005,0.001338796,0.001075223,0.001663473,0.00239984,0.002228271,0.003346996,0.002334702,0.01838728],"category_scores_gemma":[0.00606985,0.002014745,0.003123222,0.003553723,0.001679908,0.005480225,0.002323891,0.002975135,0.001538628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007443751,"about_ca_system_score_gemma":0.001372254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002744346,"about_ca_topic_score_gemma":0.0005236797,"domain_scores_codex":[0.9839903,0.0008555857,0.00431077,0.002366726,0.005448228,0.003028457],"domain_scores_gemma":[0.985854,0.004564835,0.004911384,0.002216457,0.0007412707,0.001712015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005646903,0.001373448,0.000409582,0.001154374,0.008851127,0.008143383,0.01039693,0.09386708,0.005531332,0.8413774,0.01184367,0.01140478],"study_design_scores_gemma":[0.002840068,0.001735698,0.0007451221,0.001674364,0.004378688,0.00188123,0.003076457,0.7021872,0.2754785,0.00304356,0.0008296786,0.002129412],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9499758,0.00224897,0.024148,0.0006805598,0.00327022,0.00009092897,0.0004737596,0.0001941389,0.01891755],"genre_scores_gemma":[0.9712462,0.002663394,0.02055183,0.0005899863,0.003361217,0.0004705643,0.0002055689,0.0006074584,0.0003037387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8383338,"threshold_uncertainty_score":0.9999363,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102538225","doi":"10.2307/3315915","title":"Set estimation and nonparametric detection","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":79,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Mathematics; Estimator; Combinatorics; Statistics; Algorithm; Applied mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07493561435641664,"gpt":0.3645627467966709,"spread":0.2896271324402542,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006944994,0.00007913374,0.0001762362,0.0004456438,0.0001782263,0.0002135412,0.0001853547,0.00004155378,0.0005277261],"category_scores_gemma":[0.007589788,0.00006767574,0.00001748113,0.0005793591,0.0001235954,0.0003180404,0.000004223577,0.0001877633,0.00006743411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024872,"about_ca_system_score_gemma":0.0002943625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003570981,"about_ca_topic_score_gemma":0.00251248,"domain_scores_codex":[0.9986054,0.00005667215,0.0005380738,0.0001246579,0.000482726,0.0001925097],"domain_scores_gemma":[0.9974456,0.001206275,0.0002188526,0.0001076563,0.0004563767,0.0005652124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009871439,0.000001663688,0.001099934,0.000003734864,0.000004521152,0.000106747,0.0002354575,0.002972466,0.00000765766,0.001038785,0.0009101424,0.993609],"study_design_scores_gemma":[0.0006979085,0.0004633697,0.05194042,0.000052871,0.00005815835,0.0008563122,0.0006747555,0.1319188,0.0002749341,0.7834688,0.02924837,0.0003452947],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08453143,0.0002050236,0.9141826,0.0000734812,0.0003263279,0.00003994829,0.0001665491,0.00000299832,0.0004716572],"genre_scores_gemma":[0.8913705,0.0000196296,0.1082302,0.0000381843,0.0000732474,4.168587e-7,0.000001104489,0.000007084672,0.0002596187],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9932637,"threshold_uncertainty_score":0.9086233,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W126006283","doi":"10.1080/00224065.2000.11979996","title":"Monitoring Processes with Highly Censored Data","year":2000,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Censoring (clinical trials); Control chart; Computer science; Reliability engineering; Statistical process control; Statistics; Process (computing); Shewhart individuals control chart; Mathematics; Engineering; EWMA chart","retraction":null,"screen_n_in":null,"score":{"opus":0.2433913843936824,"gpt":0.4970511335271001,"spread":0.2536597491334177,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002368434,0.0001605198,0.0005535289,0.0004182071,0.0001427624,0.0001114513,0.002697414,0.0001463153,0.0001237567],"category_scores_gemma":[0.01755065,0.000103349,0.00003121501,0.001788844,0.0003505728,0.001022147,0.0001898433,0.0005444438,0.00005877717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000054741,"about_ca_system_score_gemma":0.000275894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005982803,"about_ca_topic_score_gemma":0.00001032322,"domain_scores_codex":[0.9962922,0.0001226611,0.001327694,0.0004240651,0.001502828,0.0003305544],"domain_scores_gemma":[0.9945845,0.002005799,0.0008889305,0.001107858,0.001284642,0.0001282727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005128136,0.0002002266,0.05411338,0.00006418317,0.00006995483,0.0002354161,0.000219409,0.0004059644,0.00117867,0.003485305,0.0005189623,0.9389957],"study_design_scores_gemma":[0.003365367,0.00178447,0.03732159,0.0007990045,0.0001275325,0.001422169,0.00730356,0.0001933416,0.04851903,0.7970694,0.1010347,0.001059873],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8685435,0.001802743,0.1183488,0.009202998,0.0004912858,0.0001346545,0.00005128853,0.0001555253,0.001269206],"genre_scores_gemma":[0.908273,0.0002024691,0.09088285,0.00003608382,0.0002541583,0.000001727477,5.724506e-7,0.00001443983,0.0003346375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9379358,"threshold_uncertainty_score":0.9907249,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124599093","doi":"10.1002/qre.1012","title":"On t and EWMA t charts for monitoring changes in the process mean","year":2009,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"EWMA chart; Control chart; Standard deviation; X-bar chart; Chart; \\bar x and R chart; Statistics; Robustness (evolution); Mathematics; Moving average; Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.09785959620950711,"gpt":0.447518173055188,"spread":0.3496585768456809,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002393409,0.0001045144,0.0001508048,0.00009548567,0.00007536585,0.0001242569,0.0002767346,0.00004750485,0.000004184659],"category_scores_gemma":[0.007106753,0.00007034104,0.00002199087,0.0001314342,0.00003675471,0.0001655525,0.00002253743,0.0001622631,9.980341e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003443506,"about_ca_system_score_gemma":0.000007494617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004874026,"about_ca_topic_score_gemma":0.000002567518,"domain_scores_codex":[0.9985428,0.00004402391,0.0003243515,0.0003434515,0.0005932339,0.000152133],"domain_scores_gemma":[0.996981,0.002592399,0.00006547367,0.0001648468,0.0001478331,0.00004839351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0007502532,0.0007429458,0.08476432,0.000457873,0.00004108917,0.00001256487,0.02232406,0.1043471,0.002358012,0.4578221,0.0001693701,0.3262103],"study_design_scores_gemma":[0.000729755,0.0002558454,0.508656,0.0001393802,0.000005434309,0.000006502463,0.001171973,0.04755903,0.001361202,0.4384069,0.00139762,0.0003103538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.93841,0.00006244379,0.05348418,0.006990488,0.0005619775,0.0002494958,0.00002496391,0.00003460651,0.0001818102],"genre_scores_gemma":[0.9972261,0.00001319416,0.002371689,0.0001180044,0.0002017104,0.0000315942,0.000001616019,0.000004069552,0.00003203827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4238917,"threshold_uncertainty_score":0.850796,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2131080154","doi":"10.1002/qre.1171","title":"A new non‐parametric CUSUM mean chart","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"CUSUM; Control chart; X-bar chart; Statistics; Shewhart individuals control chart; Chart; EWMA chart; Parametric statistics; Computer science; Statistical process control; \\bar x and R chart; Normality; Mathematics; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.06312127271983545,"gpt":0.4176280458042642,"spread":0.3545067730844287,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002238862,0.0001447699,0.0002308192,0.0002134152,0.0000736501,0.0001918856,0.0005380217,0.0001056441,0.0003318246],"category_scores_gemma":[0.01758608,0.0001189801,0.00007007647,0.0003977021,0.0000624444,0.0003649591,0.0001559496,0.0004953035,0.00008698244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000040674,"about_ca_system_score_gemma":0.00004747684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009177509,"about_ca_topic_score_gemma":0.00001136953,"domain_scores_codex":[0.9975523,0.00003100749,0.0006415549,0.0005079451,0.001056246,0.0002109143],"domain_scores_gemma":[0.9967554,0.002153077,0.0001174809,0.0004064432,0.0003164673,0.0002511744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002582363,0.0005075169,0.1777337,0.0002036925,0.0001376664,0.00002453784,0.002675009,0.05459935,0.02863595,0.3910958,0.00541208,0.3387165],"study_design_scores_gemma":[0.0009489948,0.00007112334,0.560011,0.0000354936,0.00001333936,0.0000226771,0.0001778947,0.1813705,0.002875121,0.1659113,0.08786999,0.0006925274],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3406193,0.00001672659,0.6531113,0.001536323,0.003240616,0.00009901878,0.0000255038,0.00008618784,0.001264976],"genre_scores_gemma":[0.9277458,0.000004875003,0.07072873,0.00005012773,0.0004448378,0.000006703501,0.000003719099,0.00000987819,0.001005283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5871266,"threshold_uncertainty_score":0.9906892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2028104463","doi":"10.1080/0266476042000285503","title":"Monitoring Process Mean and Variability with One Non-central Chi-square Chart","year":2004,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"EWMA chart; Control chart; Chart; Statistics; Statistic; X-bar chart; Process (computing); Statistical process control; Mathematics; Control limits; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.05249982508359512,"gpt":0.3654536126362057,"spread":0.3129537875526106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001376533,0.000240469,0.0005851848,0.0001555542,0.0002259341,0.0002274179,0.0004584484,0.00007593363,0.0000207175],"category_scores_gemma":[0.0009421955,0.0001752733,0.00003266648,0.0004610938,0.0003003916,0.0003652327,0.00006681461,0.0005415178,0.000008735338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001679141,"about_ca_system_score_gemma":0.0003050741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002444968,"about_ca_topic_score_gemma":0.000004094802,"domain_scores_codex":[0.9960557,0.00003508193,0.001072651,0.0003969115,0.0019935,0.000446209],"domain_scores_gemma":[0.9966263,0.0008721693,0.000836768,0.0002796677,0.0009530777,0.0004319771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005410284,0.002224898,0.08578268,0.001036724,0.0005593388,0.000811223,0.03068251,0.1207158,0.004141383,0.1717191,0.0002625001,0.5766535],"study_design_scores_gemma":[0.003511471,0.000829104,0.06148723,0.0003460053,0.0001536514,0.0001427784,0.004860111,0.00110503,0.009436501,0.9173961,0.0001391551,0.0005929093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1833601,0.0000301451,0.8156129,0.00008239873,0.0002717829,0.0001716268,0.00006585978,0.00001318498,0.0003920153],"genre_scores_gemma":[0.7083018,0.00002196434,0.2913246,0.00001476118,0.0003015028,0.000004268653,6.572711e-7,0.00001942951,0.00001102323],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7456769,"threshold_uncertainty_score":0.7147439,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167311767","doi":"10.1109/tbme.2006.877107","title":"Adaptive Change Detection in Heart Rate Trend Monitoring in Anesthetized Children","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"Change detection; Kalman filter; Algorithm; Noise (video); CUSUM; Computer science; Adaptive filter; Receiver operating characteristic; Forgetting; Signal processing; Artificial intelligence; Mathematics; Statistics; Machine learning; Digital signal processing","retraction":null,"screen_n_in":null,"score":{"opus":0.04692509954003388,"gpt":0.3230288260065739,"spread":0.27610372646654,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007459242,0.000220458,0.0003344936,0.001103721,0.00007607581,0.00005369288,0.0002287755,0.0001509668,0.00002233209],"category_scores_gemma":[0.000115209,0.0002040841,0.00007623097,0.002191823,0.00007030091,0.0003691936,0.000002792521,0.000543515,0.00003686677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002247893,"about_ca_system_score_gemma":0.00001625166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003500171,"about_ca_topic_score_gemma":0.0001412541,"domain_scores_codex":[0.9975654,0.00008024017,0.0006450105,0.0005091169,0.0007493146,0.0004508689],"domain_scores_gemma":[0.9987833,0.0007564027,0.00005863859,0.0002204726,0.00003343598,0.0001477043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003447084,0.0007613444,0.002750337,0.0000227027,0.00002116752,0.0001541842,0.0005405436,0.3854428,0.04810319,0.00007141686,0.000009646769,0.5617779],"study_design_scores_gemma":[0.005290816,0.0006143076,0.4932708,0.0005635208,0.00002726515,0.0001008377,0.000345114,0.3883379,0.1059744,0.002969686,0.001110257,0.001395184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2893074,0.00006263801,0.7093545,0.0001172159,0.0008259495,0.000196344,0.00001620613,0.0001052465,0.00001448319],"genre_scores_gemma":[0.9957628,0.0000128447,0.003779189,0.00001024775,0.0002451383,0.0001262504,0.000001364148,0.00002708211,0.00003506888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7064553,"threshold_uncertainty_score":0.832231,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2765232457","doi":"10.1080/00224065.2017.11918004","title":"Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart","year":2017,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Universiti Tunku Abdul Rahman","keywords":"EWMA chart; Statistics; Coefficient of variation; Control chart; Mathematics; Chart; Sampling (signal processing); Interval (graph theory); Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.3972268170910757,"gpt":0.5445713808126019,"spread":0.1473445637215263,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007158302,0.0001120913,0.0004920549,0.0003181534,0.0005153723,0.0001736711,0.001786737,0.0001412296,0.0000173156],"category_scores_gemma":[0.03570081,0.0000722264,0.00009611899,0.0003893684,0.0003755693,0.0004521549,0.0003952378,0.0005029842,0.000003680805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117714,"about_ca_system_score_gemma":0.0001199587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003557832,"about_ca_topic_score_gemma":0.000001482452,"domain_scores_codex":[0.9968216,0.0001649925,0.00150871,0.0002115629,0.001053792,0.0002393176],"domain_scores_gemma":[0.992906,0.001615432,0.00328594,0.0008070325,0.001330463,0.00005508223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004449576,0.0004674033,0.1117059,0.0001059565,0.0002436073,0.00003071024,0.002822664,0.02254311,0.3420842,0.2057777,0.00004314074,0.3137306],"study_design_scores_gemma":[0.001020077,0.000360552,0.06923969,0.0005235612,0.00008421273,0.0001660812,0.005058709,0.009833367,0.04819559,0.8636689,0.001560372,0.0002889206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3576573,0.0001185747,0.6397985,0.001213831,0.001076479,0.00005024771,0.000004121033,0.00001016359,0.00007081965],"genre_scores_gemma":[0.895248,0.00000983575,0.104472,0.00001270885,0.0002288375,0.000001075659,3.531488e-8,0.000007543492,0.00001998227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6578911,"threshold_uncertainty_score":0.9724219,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057931901","doi":"10.1214/13-aoas684","title":"Beta regression for time series analysis of bounded data, with application to Canada Google® Flu Trends","year":2014,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Bounded function; Autoregressive model; Mathematics; Econometrics; Statistics; Autoregressive integrated moving average; Regression analysis; Time series; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.1102783874309793,"gpt":0.4260513457302483,"spread":0.315772958299269,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001452133,0.0001724518,0.000616824,0.0002206737,0.0001804656,0.00004802284,0.001273742,0.00003164459,0.00003208237],"category_scores_gemma":[0.001097169,0.000103817,0.00003160731,0.001572036,0.0001974144,0.00008947631,0.0002006757,0.00007725266,0.000004691998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001958248,"about_ca_system_score_gemma":0.0001523039,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003887814,"about_ca_topic_score_gemma":0.04053688,"domain_scores_codex":[0.9972239,0.00005312095,0.0007394939,0.0004823553,0.00123718,0.0002639812],"domain_scores_gemma":[0.993551,0.0034653,0.0006522527,0.001444438,0.0007579629,0.0001290643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001689504,0.00008747917,0.0002206128,0.00006814502,0.0005326014,0.000001005041,0.0006066966,0.0301188,0.001800302,0.2213292,0.03027077,0.7132748],"study_design_scores_gemma":[0.001018011,0.0008197451,0.01472136,0.00006472851,0.001655466,0.000001647598,0.001282276,0.3332158,0.0251427,0.4515965,0.1695379,0.0009437572],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003132759,0.00001683618,0.9891651,0.0007094865,0.00003364999,0.0002461215,0.005546974,0.0000119691,0.001137118],"genre_scores_gemma":[0.7515092,0.000006294769,0.2468514,0.0002161142,0.0000593539,0.00005203658,0.0005470035,0.00002348016,0.000735034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7483765,"threshold_uncertainty_score":0.9769709,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107951460","doi":"10.1016/s0167-7152(01)00183-3","title":"On the average run lengths of quality control schemes using a Markov chain approach","year":2002,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada; University of Manitoba","funders":"","keywords":"CUSUM; EWMA chart; Markov chain; Mathematics; Control chart; Standard deviation; Variance (accounting); Statistics; Moving average; Mathematical optimization; Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.1524036941183091,"gpt":0.3777680603669782,"spread":0.2253643662486691,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004774095,0.0003231476,0.0006485334,0.0001201216,0.0003543429,0.0001542375,0.0009413963,0.00008418863,0.0003887395],"category_scores_gemma":[0.03568132,0.0002194844,0.0001256071,0.0005901937,0.0009696561,0.0002058728,0.0001387417,0.0004113242,0.0000430999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001873761,"about_ca_system_score_gemma":0.000040767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004728838,"about_ca_topic_score_gemma":0.000005969019,"domain_scores_codex":[0.9938102,0.001190547,0.001416513,0.0008787301,0.002145895,0.0005581651],"domain_scores_gemma":[0.9812775,0.01626285,0.0006440616,0.001209382,0.0004529056,0.0001533305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002541148,0.0005938947,0.005317106,0.0002320457,0.0000919321,0.00001611758,0.001454374,0.005284696,0.004710664,0.943515,0.003866474,0.03466358],"study_design_scores_gemma":[0.0007926966,0.00008645831,0.00269841,0.00003653463,0.00003125035,0.000003168577,0.00007779556,0.1225621,0.0003083784,0.8726769,0.000337469,0.0003888446],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09414992,0.00003885463,0.9019504,0.00159909,0.0002289785,0.0006838087,0.0006644414,0.00003834833,0.0006462181],"genre_scores_gemma":[0.6664493,0.000001751553,0.3324777,0.0009121964,0.00005803664,0.00002976948,0.000003622609,0.00001875843,0.00004887063],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5722994,"threshold_uncertainty_score":0.9724416,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164663271","doi":"10.1142/s0218539309003277","title":"THE EXACT RUN LENGTH DISTRIBUTION AND DESIGN OF THE S<sup>2</sup> CHART WHEN THE IN-CONTROL VARIANCE IS ESTIMATED","year":2009,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Centre National de la Recherche Scientifique; University of Calgary","keywords":"Control chart; Control limits; Variance (accounting); Statistics; Mathematics; Standard deviation; Set (abstract data type); Statistical process control; Process (computing); Control (management); Distribution (mathematics); Computer science; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.05136140508124953,"gpt":0.3641453237932744,"spread":0.3127839187120248,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007574971,0.0001121126,0.0002457022,0.00003878651,0.0001447297,0.000122519,0.0006869551,0.00005403606,0.000005928888],"category_scores_gemma":[0.01625618,0.00005582333,0.00007233599,0.0001640969,0.0001515917,0.0003566952,0.00006573633,0.0004030553,4.66476e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001046546,"about_ca_system_score_gemma":0.00005752448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001596553,"about_ca_topic_score_gemma":0.000001287055,"domain_scores_codex":[0.9972841,0.0002724306,0.001078806,0.0001752777,0.001039364,0.0001500062],"domain_scores_gemma":[0.9912332,0.007405272,0.0004240873,0.0002411311,0.0006389132,0.00005743823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008452545,0.00008081402,0.01464894,0.00001956801,0.00007022092,0.000005220823,0.002030062,0.8829775,0.0004848688,0.02044312,0.00008990711,0.07830456],"study_design_scores_gemma":[0.0008306328,0.0000726657,0.4385571,0.0001574927,0.0000152179,0.00003507945,0.0003030314,0.4221773,0.0002472043,0.1363596,0.00113305,0.0001115686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1252921,0.0004388407,0.8592986,0.01446945,0.0002629113,0.0001616083,0.00005545552,0.000006225531,0.00001473021],"genre_scores_gemma":[0.9971151,0.000202452,0.002483453,0.0001003774,0.00007943405,0.000001644208,4.625784e-7,0.000003336308,0.00001377892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.871823,"threshold_uncertainty_score":0.9920303,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1969946313","doi":"10.1080/02664760802192981","title":"Diagnostics of prior-data agreement in applied Bayesian analysis","year":2008,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Bayesian probability; Prior probability; Computer science; Bayesian hierarchical modeling; Bayesian average; Reliability (semiconductor); Bayesian statistics; Bayesian experimental design; Data mining; Statistics; Bayesian inference; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1399333525279581,"gpt":0.4126900095050708,"spread":0.2727566569771127,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002540691,0.0002236558,0.001079643,0.0009443468,0.0001114104,0.00004952113,0.001639668,0.00008841977,0.0001652445],"category_scores_gemma":[0.007413644,0.0001817696,0.00008186315,0.002199556,0.0002777363,0.0001594127,0.0003256084,0.0004432877,0.00002174628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206206,"about_ca_system_score_gemma":0.0002867333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005226344,"about_ca_topic_score_gemma":0.00006595433,"domain_scores_codex":[0.9937767,0.00006543552,0.002653301,0.0004306343,0.002685617,0.0003883254],"domain_scores_gemma":[0.988761,0.007514752,0.001911135,0.0009169556,0.0006338477,0.000262288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0019291,0.002116108,0.07876366,0.0001966139,0.001696195,0.002190969,0.007457164,0.1297222,0.001363113,0.19612,0.02451045,0.5539344],"study_design_scores_gemma":[0.005334874,0.0005052462,0.1913335,0.0001015879,0.001670568,0.00008939314,0.005215314,0.06214217,0.002382662,0.7226063,0.00736405,0.00125432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01076373,0.0001209409,0.9857149,0.00003635182,0.0002066342,0.0001514044,0.0005163587,0.000005253098,0.002484432],"genre_scores_gemma":[0.6493223,0.0002032407,0.3503088,0.00002364028,0.00008989112,0.000002075755,0.00001513559,0.00001179244,0.00002307448],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6385586,"threshold_uncertainty_score":0.887536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001297076","doi":"10.1080/07408170309342346","title":"Optimal production control problem in stochastic multiple-product multiple-machine manufacturing systems","year":2003,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec; Université du Québec à Montréal","funders":"","keywords":"Parameterized complexity; Mathematical optimization; Production (economics); Optimal control; Product (mathematics); Production control; Computer science; Product type; Control variable; Control (management); Holding cost; Engineering; Mathematics; Algorithm; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.05826688806782515,"gpt":0.3415631655700667,"spread":0.2832962775022415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001508745,0.000316205,0.0004872518,0.000459222,0.0004254511,0.0001849147,0.0003590921,0.0000894779,0.00009013658],"category_scores_gemma":[0.003978522,0.0002725115,0.0001034961,0.0007051406,0.0001384983,0.0008383105,0.000006977879,0.000590788,0.00011491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000208124,"about_ca_system_score_gemma":0.00008168628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000160239,"about_ca_topic_score_gemma":0.0002594177,"domain_scores_codex":[0.9960011,0.0003237686,0.001046185,0.001041798,0.0009887132,0.0005983881],"domain_scores_gemma":[0.9965988,0.002116171,0.000227136,0.0006005618,0.0002528315,0.0002045282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000122173,0.0001612338,0.0008968005,0.00002450466,0.00002053035,0.00001020059,0.0002874518,0.9869471,0.001549368,0.000109779,0.00001225891,0.009858622],"study_design_scores_gemma":[0.01146245,0.0003881801,0.01690149,0.0004523639,0.0002389229,0.0004535513,0.004736778,0.9079357,0.03691675,0.01135991,0.006583542,0.002570416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04224874,0.0003726997,0.9538223,0.0002286221,0.001816708,0.001086928,0.00007007633,0.0001522097,0.0002017195],"genre_scores_gemma":[0.9817741,0.000006276783,0.0169848,0.000009448283,0.0001346181,0.0002722502,0.000003013104,0.00003977277,0.0007757255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9395254,"threshold_uncertainty_score":0.9999727,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003842257","doi":"10.1002/qre.428","title":"A CCC‐<i>r</i> chart for high‐yield processes","year":2001,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Chart; X-bar chart; \\bar x and R chart; Control limits; Control chart; Mathematics; Statistics; Function (biology); Fraction (chemistry); Mathematical optimization; Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.1228190807706486,"gpt":0.423784452895995,"spread":0.3009653721253464,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001604441,0.0001329145,0.0002221979,0.00009332447,0.00009572921,0.0001381991,0.0003752894,0.00007206695,0.00008238447],"category_scores_gemma":[0.03296072,0.0001091335,0.00004731486,0.0002406836,0.00006009901,0.0003802137,0.00008747028,0.0001407177,0.00001463536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004936861,"about_ca_system_score_gemma":0.00003651777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002891299,"about_ca_topic_score_gemma":0.00000548611,"domain_scores_codex":[0.9980056,0.00002283165,0.0006005565,0.0004718188,0.0006935813,0.0002056358],"domain_scores_gemma":[0.9943851,0.004495161,0.0001111849,0.0002411597,0.0006579979,0.0001093434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001901699,0.001310458,0.1550535,0.001859225,0.0002434072,0.00003378346,0.002476317,0.1280186,0.007591985,0.5441654,0.006026656,0.1513189],"study_design_scores_gemma":[0.001709139,0.0003017853,0.1459338,0.0002247423,0.00002817504,0.00004128558,0.0005141053,0.06084775,0.004315557,0.5754462,0.2095191,0.001118361],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1667298,0.00007844446,0.8275303,0.003689014,0.001160174,0.0001929887,0.00009930941,0.0001034265,0.0004166102],"genre_scores_gemma":[0.9754952,0.00004011458,0.02309246,0.0001341673,0.0003616396,0.00006186686,0.00001069439,0.00001108197,0.0007928322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8087654,"threshold_uncertainty_score":0.975185,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2082059519","doi":"10.1016/j.jom.2005.04.003","title":"Measuring performance in multi‐stage service operations: An application of cause selecting control charts","year":2005,"lang":"en","type":"article","venue":"Journal of Operations Management","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":58,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Control chart; Chart; Computer science; Shewhart individuals control chart; Cascade; Service (business); Context (archaeology); Process (computing); Control (management); Process management; Operations research; Statistics; Engineering; EWMA chart; Artificial intelligence; Business; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1814158265438433,"gpt":0.4251227438609909,"spread":0.2437069173171476,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00229083,0.0001177687,0.0002639972,0.0004777497,0.0002208753,0.0001730116,0.0004696915,0.00003160095,0.00003439774],"category_scores_gemma":[0.0005296154,0.00009909116,0.00003493994,0.0007973683,0.00002325006,0.002051625,0.00005396266,0.0001976654,0.00002581578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001667296,"about_ca_system_score_gemma":0.00005256888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000227405,"about_ca_topic_score_gemma":0.0006531292,"domain_scores_codex":[0.997289,0.0001253913,0.001297124,0.0002301956,0.0008782257,0.0001800767],"domain_scores_gemma":[0.9980748,0.0001476959,0.0002894601,0.0002980058,0.001101152,0.0000888892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002612253,0.0001901728,0.006016583,0.00002253575,0.00002060516,0.000003844428,0.000741499,0.9520374,0.002024719,0.001328819,0.000004053249,0.03758368],"study_design_scores_gemma":[0.001269781,0.00008479143,0.03113294,0.0000664801,0.00002739554,0.000009017444,0.001521428,0.9631953,0.001667474,0.000080265,0.0008148552,0.000130335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3433456,0.00006746135,0.6555307,0.0004449794,0.00008765328,0.0003042405,0.000004278415,0.000008020515,0.0002071241],"genre_scores_gemma":[0.8975158,0.00003344434,0.1019761,0.0001179179,0.0001041764,0.00002970824,0.000001230206,0.000009966117,0.0002116478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5541702,"threshold_uncertainty_score":0.404082,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2004722584","doi":"10.1016/j.ejor.2012.11.031","title":"Economic and economic-statistical designs of an control chart for two-unit series systems with condition-based maintenance","year":2012,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":53,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Control chart; Markov chain; Chart; Computer science; Mathematical optimization; Series (stratigraphy); Shewhart individuals control chart; Reliability engineering; Constraint (computer-aided design); Failure rate; Statistics; Mathematics; EWMA chart; Engineering; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.2263839827036488,"gpt":0.4747020835690045,"spread":0.2483181008653557,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01094846,0.0001091365,0.0003140552,0.0002365182,0.000266794,0.0002930828,0.00037287,0.0000156717,0.0001522353],"category_scores_gemma":[0.003060878,0.00007612119,0.00003016991,0.00007202564,0.0004822142,0.001184769,0.00002957008,0.0002307055,0.00004712939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000981005,"about_ca_system_score_gemma":0.0004494417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006718062,"about_ca_topic_score_gemma":0.000007828882,"domain_scores_codex":[0.99676,0.001119699,0.0008236787,0.0002060167,0.0007644565,0.0003261753],"domain_scores_gemma":[0.9924634,0.005390977,0.0003055865,0.0001670208,0.001323946,0.0003491486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008264913,0.0003295126,0.0280952,0.0001204621,0.0001862834,0.0001467778,0.0006675788,0.3091266,0.005433816,0.6318118,0.004546878,0.01127019],"study_design_scores_gemma":[0.03413539,0.02359077,0.2469833,0.0009428831,0.0001611231,0.002341876,0.01077916,0.5957931,0.009518273,0.03511663,0.03884863,0.001788761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1498171,0.0001448333,0.8473415,0.0005798369,0.00027635,0.0003545864,0.0005808629,0.000004369938,0.0009005679],"genre_scores_gemma":[0.9712939,0.000003292967,0.02788208,0.00002928968,0.0005508222,0.00001002502,0.000008662268,0.00002138757,0.0002006006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8214768,"threshold_uncertainty_score":0.379454,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1914109520","doi":"10.1007/978-3-642-57590-7_17","title":"Detecting Changes in the Mean from Censored Lifetime Data","year":2001,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Censoring (clinical trials); Control chart; Reliability engineering; Statistics; Computer science; Process (computing); Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2779142949213244,"gpt":0.4254626745535473,"spread":0.1475483796322229,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002082149,0.0003456573,0.0005334447,0.0002407246,0.0001620706,0.0003202536,0.004030851,0.0002413065,0.002679655],"category_scores_gemma":[0.005623399,0.0002061859,0.00004821545,0.0001733485,0.0001445392,0.0002813767,0.0009566552,0.0007424883,0.0008733221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005248078,"about_ca_system_score_gemma":0.00003984192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002161919,"about_ca_topic_score_gemma":0.004396109,"domain_scores_codex":[0.9954389,0.00009505877,0.0007451947,0.001257723,0.002098243,0.0003648867],"domain_scores_gemma":[0.9881667,0.008726722,0.0003827775,0.002460901,0.0001625992,0.0001003101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000050509,0.00002082219,0.0002198053,0.000008361129,0.00003706929,0.0003390395,0.001106053,0.0000481214,0.00003892091,0.0416735,0.006180129,0.9502777],"study_design_scores_gemma":[0.0001762494,0.00003017314,0.0001596412,0.0001047607,0.00002606316,0.0000075163,0.0008482268,0.001615401,0.00003284201,0.6108687,0.3857363,0.0003941527],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00004849286,0.0007249109,0.06105232,0.001086374,0.00064079,0.0003574113,0.0005963811,0.00009105515,0.9354023],"genre_scores_gemma":[0.07344136,0.0005850536,0.06654073,0.00180161,0.004864927,0.00002927193,0.0003618361,0.0002309796,0.8521442],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9498835,"threshold_uncertainty_score":0.9999046,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107954709","doi":"10.1109/9.948477","title":"Production and maintenance control for manufacturing systems","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Preventive maintenance; Failure rate; State (computer science); Production (economics); Markov process; Reliability engineering; Engineering; Corrective maintenance; Production control; Process (computing); Jump; Production rate; Control theory (sociology); Computer science; Control (management); Manufacturing engineering; Mathematics; Statistics; Algorithm; Artificial intelligence; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.04428784426840748,"gpt":0.3382228958423795,"spread":0.293935051573972,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001019219,0.0002227864,0.0004706903,0.000229152,0.0004125635,0.000231277,0.0002392708,0.00007710073,0.00004961088],"category_scores_gemma":[0.0008984675,0.0001730495,0.00009888812,0.0002076735,0.0001073355,0.0004074234,8.310446e-7,0.0001473766,0.00006072545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009957764,"about_ca_system_score_gemma":0.00002597638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001245683,"about_ca_topic_score_gemma":0.00001748451,"domain_scores_codex":[0.9974543,0.0001250564,0.0007349279,0.0005937599,0.0006907906,0.000401162],"domain_scores_gemma":[0.995948,0.002981296,0.0002379966,0.0004239076,0.0002496387,0.0001591321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000718186,0.0002082197,0.0001190339,0.0001375301,0.0001482449,0.00002008481,0.0002595009,0.2228707,0.002632464,0.0006835771,0.0004654286,0.771737],"study_design_scores_gemma":[0.003845391,0.0003075768,0.001114653,0.0001864846,0.000132992,0.0001183236,0.0005195148,0.9774708,0.002560931,0.0109374,0.002408577,0.000397325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02117019,0.0000761173,0.9741608,0.0009757103,0.001918727,0.001315446,0.00006918492,0.0002048513,0.0001089908],"genre_scores_gemma":[0.9949586,0.00001240769,0.002464776,0.0001071065,0.000148453,0.0005005421,2.579777e-7,0.00002553682,0.001782299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9737884,"threshold_uncertainty_score":0.7056754,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2049792611","doi":"10.1016/j.ejor.2013.02.021","title":"Economic and economic statistical designs of the synthetic <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si3.gif\" overflow=\"scroll\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy=\"true\">¯</mml:mo></mml:mrow></mml:mover></mml:mrow></mml:math> chart using loss functions","year":2013,"lang":"lv","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Universiti Sains Malaysia","keywords":"EWMA chart; Control chart; Statistics; Algorithm; Mathematics; Taguchi methods; Function (biology); Chart; Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.06859289612185412,"gpt":0.3280842810376489,"spread":0.2594913849157948,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005700786,0.0006283649,0.000365093,0.0006371833,0.002118311,0.002848098,0.002532406,0.0007433389,0.4453158],"category_scores_gemma":[0.006323228,0.0009621546,0.0009495068,0.0007022611,0.002698125,0.002697595,0.002174578,0.002193882,0.003916414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005760925,"about_ca_system_score_gemma":0.003142557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007026675,"about_ca_topic_score_gemma":0.0002880494,"domain_scores_codex":[0.9890992,0.001373064,0.002517147,0.001492984,0.003590406,0.001927168],"domain_scores_gemma":[0.9894614,0.005367156,0.001870027,0.001492872,0.0005292367,0.001279371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001837932,0.0003098239,0.00009725511,0.000468386,0.001159837,0.001126983,0.001477314,0.02163734,0.004133079,0.7936992,0.1679007,0.00615214],"study_design_scores_gemma":[0.00260802,0.002571222,0.002030962,0.001399144,0.0007464974,0.002513271,0.003724825,0.3315274,0.6477811,0.0007479569,0.002932491,0.001417179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7421428,0.0007912652,0.006626682,0.001005878,0.002470354,0.00006179465,0.0005385969,0.00004325841,0.2463193],"genre_scores_gemma":[0.9902117,0.0007442929,0.005054857,0.0005522266,0.002247774,0.0001656703,0.0001332614,0.0004385133,0.0004517109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7929512,"threshold_uncertainty_score":0.9992829,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; 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","retraction":null,"screen_n_in":null,"score":{"opus":0.0823865255387916,"gpt":0.3656976284032801,"spread":0.2833111028644885,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002496261,0.0005958856,0.001162551,0.0000934916,0.001396049,0.0009601157,0.002611613,0.000155997,0.0001834853],"category_scores_gemma":[0.009785518,0.0003963161,0.0001191882,0.001060833,0.0005366767,0.0007603575,0.0007994514,0.001036631,0.00001838825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003531201,"about_ca_system_score_gemma":0.0005144478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006252955,"about_ca_topic_score_gemma":0.00002043473,"domain_scores_codex":[0.9917884,0.0003286676,0.002152541,0.0009526439,0.003707934,0.001069779],"domain_scores_gemma":[0.9874521,0.007661668,0.001635193,0.000965987,0.001576858,0.0007081775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01706715,0.002393263,0.0932006,0.001801927,0.003920927,0.002394636,0.01626338,0.4653972,0.03674833,0.02856169,0.04713963,0.2851112],"study_design_scores_gemma":[0.0222947,0.003440033,0.04459745,0.004510808,0.003599453,0.001405113,0.03961493,0.6671967,0.01097229,0.1573202,0.03662265,0.008425646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02968196,0.0001861521,0.9658443,0.0003305099,0.0007695936,0.0004722995,0.002498411,0.00004769324,0.000169043],"genre_scores_gemma":[0.5937091,0.00003627424,0.4054075,0.0001301802,0.0005039969,0.000008001424,0.00001443884,0.00006584664,0.000124622],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5640271,"threshold_uncertainty_score":0.999904,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1975931298","doi":"10.1016/j.spl.2003.10.004","title":"A unified Markov chain approach for computing the run length distribution in control charts with simple or compound rules","year":2003,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Markov chain; Mathematics; Simple (philosophy); Variable-order Markov model; Markov chain mixing time; Distribution (mathematics); Markov model; Chain (unit); Set (abstract data type); Algorithm; Mathematical optimization; Applied mathematics; Computer science; Statistics; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.06579339114237433,"gpt":0.3437581148954503,"spread":0.277964723753076,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004329731,0.0003199093,0.0005487839,0.00007537071,0.0005259031,0.0002987368,0.0005622882,0.00006791815,0.00002043332],"category_scores_gemma":[0.01243306,0.0001929626,0.0000538038,0.000564823,0.0006495162,0.0001994895,0.00006052201,0.000368754,0.000005251366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002812883,"about_ca_system_score_gemma":0.0001480555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004507238,"about_ca_topic_score_gemma":0.00011357,"domain_scores_codex":[0.9956567,0.0007291806,0.0009871844,0.0008845149,0.001022097,0.0007202775],"domain_scores_gemma":[0.9879751,0.01045659,0.0003866084,0.0006343465,0.0004081053,0.0001392753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002749803,0.0008508924,0.04642116,0.0005095988,0.0001268651,0.00007634482,0.002314333,0.02151844,0.0003789937,0.862223,0.005593033,0.0572375],"study_design_scores_gemma":[0.004203696,0.0003302178,0.02002637,0.00004698749,0.00006325752,0.00002502263,0.0005217892,0.1423922,0.00008550518,0.8279382,0.00364202,0.0007246777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04409693,0.00001772578,0.9513282,0.0007678044,0.0001289088,0.001803056,0.001733989,0.00004511655,0.00007824833],"genre_scores_gemma":[0.6131212,6.450724e-7,0.3862694,0.0002983729,0.00004538015,0.0001064264,0.0001231227,0.00001823737,0.0000171903],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5690243,"threshold_uncertainty_score":0.9958856,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160885386","doi":"10.1002/qre.992","title":"The synthetic control chart based on two sample variances for monitoring the covariance matrix","year":2008,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Statistic; Control chart; Chart; Statistics; Covariance matrix; Mathematics; Covariance; Variance (accounting); Statistical process control; Bivariate analysis; Control limits; Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.08874728958372513,"gpt":0.4228483534791314,"spread":0.3341010638954062,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00388419,0.000149952,0.0002060267,0.00004499823,0.0006372803,0.0001425221,0.0006505335,0.0000459671,0.00001593674],"category_scores_gemma":[0.02828356,0.00008442844,0.00009235749,0.0001333036,0.0002017372,0.0001610085,0.00004626512,0.0002315818,0.000009703851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008072471,"about_ca_system_score_gemma":0.000039858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003639842,"about_ca_topic_score_gemma":0.000001462419,"domain_scores_codex":[0.997645,0.0001328207,0.0006023623,0.0004194092,0.0009538764,0.000246521],"domain_scores_gemma":[0.9674602,0.03148894,0.0001576975,0.0004408493,0.0003772552,0.00007504997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000378488,0.00008212397,0.01248492,0.00003414791,0.00003688421,0.00000204011,0.0002426378,0.8875278,0.0002096641,0.09012526,0.0001528119,0.00872327],"study_design_scores_gemma":[0.0009416823,0.00006652802,0.05619325,0.00004478103,0.000009959711,0.000005203507,0.0001040888,0.866215,0.0003244505,0.04892276,0.02695607,0.0002162532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01375847,0.00009808375,0.977351,0.005958525,0.002195947,0.0002914354,0.0002303413,0.00005536429,0.00006084891],"genre_scores_gemma":[0.9766812,0.0000219757,0.02246752,0.00007808743,0.0004692048,0.0001189873,0.000002132532,0.00001088275,0.0001500544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9629227,"threshold_uncertainty_score":0.9799016,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2897440829","doi":"10.1002/qre.2412","title":"Monitoring the ratio of two normal variables using variable sampling interval exponentially weighted moving average control charts","year":2018,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"EWMA chart; Control chart; Statistics; X-bar chart; Interval (graph theory); Chart; Control limits; Mathematics; Sampling interval; Statistical process control; Sampling (signal processing); Variable (mathematics); Computer science; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.08610558698603847,"gpt":0.4012396759512537,"spread":0.3151340889652152,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004330418,0.000192666,0.0003487167,0.0001324581,0.0002644033,0.0002282101,0.0005988875,0.00007841228,0.0001334927],"category_scores_gemma":[0.006838281,0.0001444824,0.00007940079,0.0002855666,0.0002041872,0.0006250218,0.0002398597,0.0002869993,0.000004705339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072154,"about_ca_system_score_gemma":0.00006338031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001690682,"about_ca_topic_score_gemma":0.000001862326,"domain_scores_codex":[0.9968902,0.0001801038,0.001126455,0.0004741274,0.001043172,0.0002859536],"domain_scores_gemma":[0.9949681,0.003306353,0.000334724,0.0003950344,0.0008964582,0.00009937703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004006618,0.0002163266,0.08471297,0.000180876,0.0002420863,0.000003814776,0.002668235,0.6655961,0.1439356,0.09301689,0.00001171432,0.009014756],"study_design_scores_gemma":[0.0008553897,0.00005399153,0.0303545,0.0002085777,0.00002815324,0.00001115656,0.0002128751,0.9287976,0.009823216,0.02876406,0.0005941444,0.0002963049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3411689,0.00003422562,0.6566325,0.0001105672,0.001758873,0.0001010932,0.00003326729,0.00003428544,0.0001263549],"genre_scores_gemma":[0.9069355,0.00000479644,0.0921052,0.00002304434,0.0008561617,0.000008936067,0.000002189482,0.00001366508,0.00005052041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5657666,"threshold_uncertainty_score":0.8186555,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971333466","doi":"10.1080/10485250601046752","title":"Risk comparison of some shrinkage M-estimators in linear models","year":2006,"lang":"en","type":"article","venue":"Journal of nonparametric statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Mathematics; A priori and a posteriori; Applied mathematics; Linear regression; Linear model; Subspace topology; Asymptotic distribution; Statistics; Asymptotic analysis; Sampling (signal processing); Computer science; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.09597850055443359,"gpt":0.435455462143716,"spread":0.3394769615892824,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002548889,0.0002080383,0.001012003,0.001712285,0.00007267322,0.00007674727,0.0007686252,0.0001091497,0.00003633067],"category_scores_gemma":[0.0257435,0.0001652072,0.0001098351,0.002763138,0.0001962126,0.000629793,0.00009502652,0.0006977574,0.00002107783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001467503,"about_ca_system_score_gemma":0.0001690822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009389519,"about_ca_topic_score_gemma":0.00001401444,"domain_scores_codex":[0.9935289,0.000239837,0.003119872,0.0002750264,0.002481556,0.000354833],"domain_scores_gemma":[0.9827282,0.01274399,0.002831086,0.0003269945,0.001200615,0.0001690653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001279334,0.0005053839,0.165501,0.00002873615,0.00002143532,0.0001534873,0.0001740361,0.7489779,0.00003532327,0.02346959,0.002677265,0.05832794],"study_design_scores_gemma":[0.0006772426,0.0002579459,0.03953457,0.00003841974,0.00003630566,0.00001254167,0.0001825722,0.4122529,0.0002916907,0.5464171,0.0001506971,0.0001480488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1827391,0.0007834299,0.8152805,0.00001496722,0.000655853,0.00008320314,0.0002121361,0.00000633017,0.0002244837],"genre_scores_gemma":[0.5952473,0.00003104704,0.4045309,0.000005269623,0.0001191734,7.237294e-7,0.000001216532,0.00001302021,0.00005131994],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5229475,"threshold_uncertainty_score":0.9824631,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2280804815","doi":"10.1002/qre.1968","title":"A Generally Weighted Moving Average Signed‐rank Control Chart","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"National Research Foundation","keywords":"Control chart; EWMA chart; Chart; Statistic; Shewhart individuals control chart; Statistical process control; Rank (graph theory); Statistics; \\bar x and R chart; Computer science; Wilcoxon signed-rank test; Control limits; Process (computing); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04405491515761074,"gpt":0.3629226126963945,"spread":0.3188676975387837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002355978,0.0001696254,0.0002806931,0.000124328,0.00009282677,0.000125688,0.0004415964,0.00008885143,0.0003111524],"category_scores_gemma":[0.01095286,0.0001091243,0.00008256731,0.0001413596,0.00009090132,0.0004791348,0.0001091498,0.0001520196,0.00005997996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001166019,"about_ca_system_score_gemma":0.00002963284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001680119,"about_ca_topic_score_gemma":0.000001560746,"domain_scores_codex":[0.9972667,0.0001131775,0.0007708987,0.0005619482,0.001032594,0.0002547469],"domain_scores_gemma":[0.9952871,0.003653246,0.000141579,0.0003346993,0.0004192143,0.0001642242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008687567,0.0005370755,0.1190392,0.0002250633,0.0003156096,0.00006793107,0.001335257,0.06047365,0.1546229,0.3851603,0.001201996,0.2761522],"study_design_scores_gemma":[0.005059295,0.0001818702,0.3081505,0.000266879,0.00002769361,0.00002959661,0.00009935606,0.3579491,0.005034566,0.2766152,0.04520104,0.001384925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1591195,0.00004064421,0.8356718,0.003514277,0.000977053,0.0001041656,0.00009819783,0.00009839301,0.0003759659],"genre_scores_gemma":[0.9867015,0.00001445909,0.01200383,0.0001337074,0.0003227911,0.0000190514,0.000002477409,0.00001243116,0.0007897812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8275819,"threshold_uncertainty_score":0.9973783,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2896255934","doi":"10.1016/j.cie.2018.10.016","title":"New adaptive control charts for monitoring the multivariate coefficient of variation","year":2018,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control chart; Chart; EWMA chart; Statistics; Statistical process control; Computer science; Coefficient of variation; Shewhart individuals control chart; X-bar chart; Standard deviation; Sampling (signal processing); Sample size determination; Process (computing); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1329185810163666,"gpt":0.3590442921576657,"spread":0.2261257111412991,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001115397,0.0001745194,0.0003216226,0.000143377,0.0001610624,0.0001082523,0.0005878981,0.0001035989,0.000008893021],"category_scores_gemma":[0.003650072,0.0001280366,0.00008641868,0.0005175781,0.00005618184,0.0002089954,0.0001005648,0.0002063003,0.000009744599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008412624,"about_ca_system_score_gemma":0.00008772916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000340704,"about_ca_topic_score_gemma":3.133005e-7,"domain_scores_codex":[0.9979983,0.00005241686,0.0006241464,0.0003651913,0.0006398559,0.0003201184],"domain_scores_gemma":[0.9947942,0.004020169,0.0002841169,0.0003266237,0.0004432185,0.0001317109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002985243,0.00002774943,0.0001998044,0.000005032001,0.00008346981,0.000001579298,0.001556562,0.7408652,0.007161763,0.007689586,0.0005726278,0.2415381],"study_design_scores_gemma":[0.002101793,0.0002706782,0.001705878,0.00009788415,0.00002939868,0.00000138907,0.00008790666,0.9836232,0.007908074,0.001408223,0.002575479,0.0001900767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00577884,0.00003395033,0.9871565,0.0001096851,0.006334546,0.0004871837,0.00002040798,0.00005856915,0.00002032009],"genre_scores_gemma":[0.9363769,3.698767e-7,0.05981096,0.000009466782,0.003727096,0.00001800494,6.40673e-7,0.00001868734,0.00003785521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9305981,"threshold_uncertainty_score":0.5221182,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1977477287","doi":"10.1093/imaman/dpp026","title":"Economic and economic-statistical design of a multivariate Bayesian control chart for condition-based maintenance","year":2010,"lang":"en","type":"article","venue":"IMA Journal of Management Mathematics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Control chart; Chart; Library science; Bayesian probability; Multivariate statistics; Operations research; Engineering; Management; History; Computer science; Mathematics; Artificial intelligence; Economics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.04470442270370747,"gpt":0.3753475730142514,"spread":0.330643150310544,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002622742,0.0001518334,0.0005255829,0.0002103802,0.00007610834,0.0001156455,0.0004207185,0.00004505747,0.0001505602],"category_scores_gemma":[0.001905557,0.0001174098,0.00008046295,0.00004245836,0.0001922562,0.0002590538,0.00003949747,0.0001495733,0.00001782312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005257612,"about_ca_system_score_gemma":0.00006332438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001148807,"about_ca_topic_score_gemma":0.00000280491,"domain_scores_codex":[0.9979079,0.00005462906,0.001278084,0.0002090469,0.0003312627,0.0002191196],"domain_scores_gemma":[0.9922276,0.005979228,0.001194872,0.0002438399,0.0002181774,0.0001362789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008585422,0.0003718201,0.0007029523,0.0005716691,0.0003547118,0.00007029322,0.000337672,0.04891302,0.001947654,0.9054418,0.00445234,0.03597753],"study_design_scores_gemma":[0.002507158,0.0001936849,0.0005641464,0.00007461375,0.00009465739,0.00001848477,0.0002198241,0.4963223,0.0004306116,0.4987864,0.0006529349,0.00013513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003324581,0.00001122501,0.9950956,0.0003638601,0.000413076,0.0004221472,0.0001256927,0.000006096988,0.0002377116],"genre_scores_gemma":[0.5330631,0.000003841991,0.4667654,0.00002957575,0.00005836177,0.00001089479,5.630305e-7,0.00001155803,0.00005667039],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5297385,"threshold_uncertainty_score":0.4787834,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2530922009","doi":"10.1002/qre.2087","title":"On the Performance of Shewhart median Chart in the Presence of Measurement Errors","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Control chart; Shewhart individuals control chart; Chart; Statistics; X-bar chart; Covariate; Computer science; \\bar x and R chart; Observational error; Mathematics; EWMA chart; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.1253652341891474,"gpt":0.3847240618444989,"spread":0.2593588276553516,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006431922,0.00006785899,0.0001291746,0.00005889066,0.00002281061,0.00001270284,0.0005340164,0.00002746354,0.00003174721],"category_scores_gemma":[0.0209128,0.0000286648,0.00003202882,0.0001435607,0.0001446343,0.0001369854,0.00005535176,0.0001096662,0.000002537477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004058595,"about_ca_system_score_gemma":0.00002032536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001517217,"about_ca_topic_score_gemma":0.000004583383,"domain_scores_codex":[0.9976083,0.0001196627,0.0005305549,0.0001862482,0.001458873,0.00009637477],"domain_scores_gemma":[0.9940453,0.005224817,0.0001236131,0.0002903953,0.0002910835,0.00002480679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005899812,0.0007393123,0.302701,0.0004383908,0.00007725177,0.000002957652,0.008841777,0.08504177,0.0283985,0.5175359,0.0006684432,0.05496476],"study_design_scores_gemma":[0.0004813472,0.0001632409,0.8866473,0.0004609101,0.00000452027,0.00000178279,0.0006145951,0.04307973,0.0132226,0.05361123,0.001530249,0.0001825087],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670176,0.0000157988,0.02603488,0.006289112,0.0003518382,0.0001131176,0.00001760182,0.000008778565,0.0001512941],"genre_scores_gemma":[0.9994004,0.00001416341,0.0004925175,0.00002844621,0.00002901017,0.00001356995,1.804888e-7,0.000003106061,0.00001858334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5839463,"threshold_uncertainty_score":0.9873345,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2000971219","doi":"10.1080/03610918.2012.655829","title":"A Sequential Rank-Based Nonparametric Adaptive EWMA Control Chart","year":2012,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; University of Windsor","keywords":"EWMA chart; Control chart; Nonparametric statistics; Rank (graph theory); Statistics; Econometrics; Chart; Computer science; Shewhart individuals control chart; Mathematics; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.3449152652221566,"gpt":0.5285670788746109,"spread":0.1836518136524543,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00151131,0.0001699183,0.0002910113,0.0005482424,0.0003490771,0.0001709539,0.0004410002,0.00008194133,0.00003923579],"category_scores_gemma":[0.003733415,0.0001694774,0.00003181822,0.001184191,0.0002542555,0.0005427692,0.0001279772,0.0002637508,0.00007200114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001300319,"about_ca_system_score_gemma":0.00006855844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002100435,"about_ca_topic_score_gemma":0.00001776416,"domain_scores_codex":[0.9972266,0.0006142077,0.0008759579,0.0002943497,0.0006992905,0.0002895837],"domain_scores_gemma":[0.9853108,0.01291499,0.0003747852,0.0006098726,0.0006347491,0.0001548384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009629315,0.0002154104,0.01961954,0.000007892989,0.00001159533,9.688645e-7,0.0007821709,0.7185513,0.00001789796,0.04496527,0.00007317593,0.2156584],"study_design_scores_gemma":[0.001097565,0.00004316408,0.04590237,0.00001539621,0.00001871256,9.450985e-7,0.0002485287,0.8994557,0.000008169398,0.0525574,0.0004877941,0.0001642425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004942611,0.0004133918,0.9931925,0.0001447437,0.0002253936,0.0003852081,0.0001664154,0.00004916012,0.0004806103],"genre_scores_gemma":[0.7866282,0.00001046368,0.213051,0.0001172869,0.00004280911,0.00003565407,0.0000775681,0.00001357497,0.00002337417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7816857,"threshold_uncertainty_score":0.6911089,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1997719892","doi":"10.1081/qen-200056484","title":"Graphical Representation of Run Length Distributions","year":2005,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Chart; Control chart; Computer science; Representation (politics); Distribution (mathematics); Statistics; Algorithm; Data mining; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1446130501748025,"gpt":0.4720399405972798,"spread":0.3274268904224773,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0009482662,0.00008659049,0.0002245132,0.0001123421,0.00005615191,0.00003587228,0.0002628806,0.00004625531,0.00005420592],"category_scores_gemma":[0.01050215,0.00007807215,0.00007537488,0.0007440357,0.00005274437,0.0003282238,0.0000600459,0.0001467192,0.00003140886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000410177,"about_ca_system_score_gemma":0.00001343962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001215547,"about_ca_topic_score_gemma":0.000004199837,"domain_scores_codex":[0.9980327,0.00005828762,0.0006939584,0.0002598747,0.0007719425,0.000183233],"domain_scores_gemma":[0.9973527,0.001873127,0.0001263669,0.000351721,0.0002002514,0.00009581802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003894745,0.0001762853,0.01950126,0.00005821387,0.00004062643,0.000003449314,0.0007272577,0.1500556,0.04669573,0.5958939,0.000346452,0.1864623],"study_design_scores_gemma":[0.001345305,0.0001169599,0.4319577,0.0001073337,0.00004661832,0.0000130252,0.001174393,0.2648779,0.09573128,0.1702869,0.03323039,0.001112255],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1496259,0.000052881,0.8493975,0.0003079566,0.0001489242,0.00005067989,0.00003918611,0.0000622273,0.0003147896],"genre_scores_gemma":[0.9599422,0.000004326858,0.03983967,0.000006742383,0.0001293199,0.000006726879,0.00000605709,0.000006682005,0.00005828725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8103163,"threshold_uncertainty_score":0.9978328,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084870082","doi":"10.1002/qre.578","title":"Joint ―<i>X</i> and <i>R</i> Charts with Two‐stage Samplings","year":2004,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Joint (building); Control chart; Stage (stratigraphy); Sampling (signal processing); Statistics; X-bar chart; \\bar x and R chart; Mathematics; Sample (material); Sample size determination; Process (computing); Control limits; Computer science; Engineering; Geology; Chromatography; Structural engineering; Chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.09185004554668968,"gpt":0.397363065768271,"spread":0.3055130202215813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00137942,0.0001447112,0.0002214791,0.00008452061,0.00008875145,0.0001674429,0.0001838905,0.00004473036,0.00003645177],"category_scores_gemma":[0.003210754,0.0001080398,0.0000297636,0.0001392481,0.0001309436,0.0003565702,0.00009185841,0.0002139726,0.000007821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005601868,"about_ca_system_score_gemma":0.00002707581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001165091,"about_ca_topic_score_gemma":0.000009499568,"domain_scores_codex":[0.9980553,0.0000254045,0.0004896878,0.0004864061,0.0007630481,0.0001801479],"domain_scores_gemma":[0.9985546,0.0007179203,0.0001077952,0.0002302277,0.0002413182,0.0001481033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002366386,0.0002025863,0.07043178,0.0002332059,0.00006938968,0.00003406332,0.001720335,0.6174111,0.003082886,0.2836985,0.00003234937,0.02284723],"study_design_scores_gemma":[0.004360828,0.0002887561,0.6071854,0.00030325,0.00002455665,0.0001012314,0.0006699532,0.04196268,0.004686237,0.3218694,0.01732605,0.001221675],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4818145,0.00004873318,0.5155466,0.001702683,0.0003122759,0.00008734083,0.0000447811,0.00006268581,0.0003803662],"genre_scores_gemma":[0.9490238,0.00001613362,0.05058838,0.0001257852,0.0001067222,0.000006444953,0.000003730577,0.000008306881,0.0001206438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5754484,"threshold_uncertainty_score":0.4405735,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2042255463","doi":"10.1080/07474940801989111","title":"Minimax Methods for Multihypothesis Sequential Testing and Change-Point Detection Problems","year":2008,"lang":"en","type":"article","venue":"Sequential Analysis","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"McGill University","keywords":"Minimax; Mathematics; Change detection; Point (geometry); Monte Carlo method; Algorithm; Decision theory; Infinity; Statistical hypothesis testing; Sequential analysis; Mathematical optimization; Applied mathematics; Computer science; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.4089770643454758,"gpt":0.4801689679845703,"spread":0.07119190363909456,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002336859,0.000250793,0.0006392754,0.0007359055,0.000701194,0.000176455,0.0003384986,0.000115187,0.00007737713],"category_scores_gemma":[0.01184236,0.0002058234,0.000312069,0.002777273,0.00025416,0.0005986363,0.0001541362,0.0001523827,0.00001671896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009355026,"about_ca_system_score_gemma":0.00003653677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006392184,"about_ca_topic_score_gemma":0.0002601343,"domain_scores_codex":[0.9967145,0.0003546773,0.0008508055,0.000940164,0.0006906695,0.0004492117],"domain_scores_gemma":[0.9942547,0.003982904,0.0004431985,0.000394582,0.0007122202,0.0002123917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006529475,0.00004812877,0.00492938,0.00003038218,0.0004568229,0.00001159748,0.001079854,0.001735452,0.05082343,0.00005662797,0.00001143205,0.9407516],"study_design_scores_gemma":[0.001246995,0.0003753545,0.02595939,0.00003750302,0.002774599,0.00007651264,0.0006677784,0.7689149,0.07814366,0.1187881,0.001896756,0.00111846],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04143595,0.0002371134,0.9573046,0.0001045763,0.0002854076,0.0003875284,0.00004116473,0.00009055848,0.0001131174],"genre_scores_gemma":[0.6041384,0.00001545106,0.3952029,0.00003073451,0.0002528526,0.0001311039,0.000003532342,0.00001761582,0.0002074],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9396331,"threshold_uncertainty_score":0.9964813,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2750424462","doi":"10.1108/jqme-06-2016-0028","title":"Bearing temperature monitoring of a Wind Turbine using physics-based model","year":2017,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"SCADA; Bearing (navigation); Turbine; Control chart; ALARM; Wind power; Engineering; Wind speed; Computer science; Reliability engineering; Control engineering; Marine engineering; Mechanical engineering; Meteorology; Artificial intelligence; Aerospace engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.184480930953304,"gpt":0.4551174038056316,"spread":0.2706364728523277,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003755741,0.0002095772,0.0007340923,0.0002481208,0.0001433708,0.0002069304,0.001097815,0.00009638477,0.000002363693],"category_scores_gemma":[0.01498491,0.0001712819,0.0001768412,0.0002884733,0.000100174,0.001095127,0.0001315367,0.0007309749,7.403092e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002205398,"about_ca_system_score_gemma":0.0001456659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002043439,"about_ca_topic_score_gemma":0.000002098937,"domain_scores_codex":[0.9964747,0.00006049129,0.001530627,0.0002601627,0.001294589,0.0003793957],"domain_scores_gemma":[0.9959931,0.0008923865,0.001536457,0.0006211622,0.0008198945,0.000137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005553217,0.0000333511,0.02302442,0.0000634091,0.0000113154,0.00002929823,0.0002515373,0.895592,0.07866967,0.0006633584,0.000002333649,0.001603822],"study_design_scores_gemma":[0.001288196,0.0000581893,0.07789171,0.001711953,0.0000173516,0.00001528261,0.0005183572,0.8728505,0.02959034,0.01570145,0.00001616301,0.0003405476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5897725,0.00009108146,0.4094488,0.00009338952,0.000512506,0.00004664565,0.000006516205,0.000006344844,0.0000222205],"genre_scores_gemma":[0.8789842,0.000007128991,0.1206033,0.000007803678,0.0003513616,7.043052e-7,9.814301e-8,0.00002079184,0.0000245757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2892117,"threshold_uncertainty_score":0.9933123,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2547541549","doi":"10.1080/03610918.2016.1252397","title":"A generally weighted moving average chart for time between events","year":2016,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"EWMA chart; Control chart; Chart; X-bar chart; Shewhart individuals control chart; Statistics; Computer science; Mathematics; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.2625290943327618,"gpt":0.5113336770883781,"spread":0.2488045827556162,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005862,0.0001409434,0.000251999,0.0002780477,0.0003354419,0.0001023652,0.000489635,0.00006784009,0.00003390464],"category_scores_gemma":[0.003423759,0.0001141769,0.00002695259,0.0003879967,0.0001171479,0.000398371,0.0002121734,0.00009566468,0.00005988995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001033299,"about_ca_system_score_gemma":0.00004767516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004432748,"about_ca_topic_score_gemma":0.000009316513,"domain_scores_codex":[0.9978765,0.0002702107,0.000842781,0.000358535,0.0004515969,0.0002004168],"domain_scores_gemma":[0.9844633,0.01397894,0.0003187814,0.0005444463,0.0006037503,0.00009074144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004616375,0.0000647299,0.01593691,0.00001357351,0.00001554505,6.0407e-7,0.0004404276,0.0560648,0.0002371571,0.02656933,0.000193181,0.9004176],"study_design_scores_gemma":[0.0005966474,0.00003263541,0.02613253,0.00003104786,0.00000734922,2.765523e-7,0.00002006373,0.6331995,0.00001132623,0.3388047,0.00104885,0.0001150292],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0152586,0.00006152853,0.9830658,0.0005291922,0.00007663898,0.000397191,0.0004069167,0.00004590055,0.0001581938],"genre_scores_gemma":[0.7198118,0.00001813958,0.2796525,0.00003344545,0.00003901602,0.00003481825,0.0001191267,0.00001388141,0.0002773207],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9003025,"threshold_uncertainty_score":0.4655999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056240178","doi":"10.2139/ssrn.958759","title":"Scaling Models for the Severity and Frequency of External Operational Loss Data","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Scaling; Econometrics; Medicine; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1236995914923106,"gpt":0.4297781318769676,"spread":0.306078540384657,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01167847,0.00009792799,0.0001731344,0.00006976734,0.0004182009,0.0001268716,0.001085022,0.00004118915,0.00001114262],"category_scores_gemma":[0.003049481,0.00006035417,0.00003879704,0.0001790398,0.0001536053,0.0009014229,0.0001613188,0.0007251504,0.000001479719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001446229,"about_ca_system_score_gemma":0.000741868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003700821,"about_ca_topic_score_gemma":0.0002380212,"domain_scores_codex":[0.9972279,0.00004724006,0.0006024248,0.0002921124,0.000879294,0.0009510255],"domain_scores_gemma":[0.9960089,0.002893176,0.0002471337,0.0003520634,0.0004171208,0.00008155564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000118245,0.00003376407,0.004723982,0.00000508241,0.00004356692,0.000003215821,0.0001685737,0.003555702,0.0006286206,0.5782091,0.00002415613,0.412486],"study_design_scores_gemma":[0.0002792292,0.00004248036,0.001334153,0.00001093514,0.00001478682,0.0002355848,0.0007503217,0.04988161,0.000187096,0.9471169,0.00007337033,0.0000736029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03734728,0.003602643,0.9583679,0.00028465,0.0001921639,0.0001024007,0.0000365265,0.00000483574,0.00006155136],"genre_scores_gemma":[0.9697725,0.0003661893,0.02932809,0.00003713657,0.0003673294,0.000001567986,0.000001727645,0.000008964191,0.0001165087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9324252,"threshold_uncertainty_score":0.404755,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002794766","doi":"10.1080/10485250410001656435","title":"On detecting jumps in time series: nonparametric setting","year":2004,"lang":"en","type":"article","venue":"Journal of nonparametric statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"Deutsche Forschungsgemeinschaft","keywords":"Mathematics; Estimator; Jump; Robustness (evolution); Algorithm; Nonparametric statistics; Filter (signal processing); Series (stratigraphy); Applied mathematics; Computer science; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.05054959789740394,"gpt":0.3888969680582198,"spread":0.3383473701608159,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004327644,0.0003310651,0.0008956364,0.004001943,0.000190422,0.0004012193,0.001229861,0.0001563647,0.0001364655],"category_scores_gemma":[0.1810813,0.000269482,0.000138141,0.009070275,0.0001507851,0.0007338271,0.0001474285,0.001190176,0.0003825885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007265313,"about_ca_system_score_gemma":0.0003470762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001402602,"about_ca_topic_score_gemma":0.000005328554,"domain_scores_codex":[0.9922741,0.0002572023,0.002609123,0.0004931771,0.003710466,0.0006559234],"domain_scores_gemma":[0.9721351,0.02386615,0.001940303,0.0004483025,0.001246814,0.0003633263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001142252,0.001101376,0.01868402,0.00009032787,0.0001166823,0.00421684,0.001134608,0.2972154,0.0006318478,0.0200484,0.0025191,0.6530991],"study_design_scores_gemma":[0.003343741,0.002232825,0.02717768,0.000341534,0.00006028441,0.0006432309,0.0006601312,0.006385054,0.001665714,0.9561561,0.0006585199,0.0006751962],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1843312,0.0001997322,0.8134893,0.0001345914,0.0009304845,0.0001697475,0.00007751756,0.0000219165,0.0006454865],"genre_scores_gemma":[0.5847331,0.00002686782,0.4147753,0.00008385893,0.0001835533,0.000002309289,8.890494e-7,0.00003084165,0.0001632277],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9361077,"threshold_uncertainty_score":0.9999757,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1970663230","doi":"10.1080/03610920903168610","title":"Control Charts for the Variance and Coefficient of Variation Based on Their Predictive Distribution","year":2010,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Control chart; Variance (accounting); Statistics; Bayesian probability; Control limits; Normal distribution; Coefficient of variation; Variation (astronomy); Gamma distribution; Chart; Distribution (mathematics); Mathematics; Computer science; Econometrics; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.06381616272995588,"gpt":0.46568498230122,"spread":0.4018688195712641,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01362014,0.0001003961,0.0002121051,0.00005802486,0.0002936727,0.00005731381,0.000344776,0.00006045491,0.00001323801],"category_scores_gemma":[0.03402366,0.00006569842,0.0000175554,0.0001944222,0.0004880056,0.00009304556,0.0000627679,0.0002455155,3.687952e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001724123,"about_ca_system_score_gemma":0.00003749022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005026086,"about_ca_topic_score_gemma":0.000004724577,"domain_scores_codex":[0.9971921,0.001774585,0.0004573002,0.0002384024,0.0002160966,0.0001214507],"domain_scores_gemma":[0.9243751,0.07432406,0.0003016466,0.0005835944,0.0003709162,0.00004469479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004508018,0.00005086077,0.0002620902,0.00001245034,0.000006545813,6.068028e-8,0.0006763082,0.00245641,0.001360545,0.8163556,0.00001760376,0.1783507],"study_design_scores_gemma":[0.0005204711,0.00007282822,0.03198553,0.00002120179,0.0000161975,4.297114e-7,0.0002003626,0.3932837,0.0004919858,0.5726647,0.0006878516,0.00005471644],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008942917,0.0001917655,0.9968941,0.0002317163,0.0002053505,0.0004614347,0.001030272,0.000009369309,0.00008171728],"genre_scores_gemma":[0.7618702,0.00002614559,0.237898,0.00005415896,0.00001546254,0.00009957352,0.00001715028,0.000005320022,0.00001400814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7609759,"threshold_uncertainty_score":0.9741132,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1902160157","doi":"10.1080/00224065.2010.11917813","title":"Assessment of a Binary Measurement System in Current Use","year":2010,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Computer science; Context (archaeology); Measure (data warehouse); Binary number; Sample (material); Quality (philosophy); Statistics; Plan (archaeology); Data mining; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.3482807538824548,"gpt":0.5453802261330982,"spread":0.1970994722506434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009795394,0.0001097984,0.0006111818,0.001000795,0.00003828104,0.00003308067,0.0008129692,0.0001412662,0.00001318382],"category_scores_gemma":[0.01633515,0.00007926082,0.00008955353,0.0009834883,0.0002115951,0.0003275711,0.0001602837,0.001035457,0.000003659319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002197683,"about_ca_system_score_gemma":0.0002601781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001034875,"about_ca_topic_score_gemma":0.00004222915,"domain_scores_codex":[0.9950524,0.0002425047,0.002135318,0.0002204801,0.00213379,0.0002155173],"domain_scores_gemma":[0.9950342,0.001033972,0.001623651,0.000465447,0.001761614,0.00008118091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008647854,0.0006818612,0.380085,0.000163831,0.00003413074,0.00009504372,0.0001307836,0.0003097865,0.1375068,0.2792196,0.000134315,0.2015524],"study_design_scores_gemma":[0.001711858,0.0006254446,0.7980316,0.0005506009,0.00003003598,0.0001504456,0.003318553,0.001509093,0.009131403,0.1789972,0.005633967,0.0003098482],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8727502,0.0002172238,0.1246305,0.0007572474,0.001422621,0.000102625,0.000007012616,0.00002123653,0.00009132921],"genre_scores_gemma":[0.9655175,0.00001026604,0.03440347,0.00000645875,0.00004926131,0.000003512177,6.421221e-8,0.000005772583,0.000003709144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4179466,"threshold_uncertainty_score":0.9919507,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}