{"meta":{"query_hash":"a5eec11a6567","filters":{"venue":"Quality and Reliability Engineering International"},"cohort_total":86,"direct_labels_cover":0,"predictions_cover":86,"exported":86,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a5eec11a6567","api":"https://metacan.xera.ac/api/v1/cohort?venue=Quality+and+Reliability+Engineering+International"},"results":[{"id":"W1491840929","doi":"10.1002/qre.1543","title":"Measurement Plan Optimization for Degradation Test Design based on the Bivariate Wiener Process","year":2013,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bivariate analysis; Test plan; Optimal design; Statistics; Design of experiments; Degradation (telecommunications); Computer science; Mathematics","score_opus":0.03524792124381177,"score_gpt":0.23859254936102187,"score_spread":0.2033446281172101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491840929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009818902,0.000016644293,0.9854904,0.0028686922,0.00045089755,0.00092772976,0.00002263317,0.00022605243,0.00017809105],"genre_scores_gemma":[0.9612063,0.000026070924,0.037768155,0.00018694786,0.00009212418,0.0006135492,0.000055823977,0.000029404668,0.000021601827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988017,0.00004363679,0.00036845013,0.0002469927,0.00035202203,0.00018718276],"domain_scores_gemma":[0.9987049,0.00054447487,0.00005528465,0.00021963361,0.0004180105,0.00005766532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012527284,0.00017815745,0.00013782587,0.000074495045,0.00009274803,0.0001177634,0.00016590356,0.00009911124,0.0000693058],"category_scores_gemma":[0.0019653444,0.00013411885,0.000053536052,0.000115697396,0.00003305291,0.00025718714,0.000010809111,0.00013872811,0.0000048066017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001738776,0.0000579272,0.00021860024,0.00013145193,0.000015039126,3.9001915e-8,0.000059670612,0.99759936,0.00029971442,0.00093608577,0.00023962453,0.0004250933],"study_design_scores_gemma":[0.0003210329,0.00005217091,0.0025619825,0.00007859313,0.000009337088,4.7070864e-7,0.000024185612,0.99478954,0.0009682223,0.0005937962,0.00042584282,0.0001748298],"about_ca_topic_score_codex":0.000018327593,"about_ca_topic_score_gemma":9.631387e-7,"teacher_disagreement_score":0.9513874,"about_ca_system_score_codex":0.0001705348,"about_ca_system_score_gemma":0.00002747964,"threshold_uncertainty_score":0.54692084},"labels":[],"label_agreement":null},{"id":"W1529669168","doi":"10.1002/qre.454","title":"An economic model for \\font\\twelveit=cmti10 scaled 1600$\\overline{\\kern‐0.85ex\\hbox{\\twelveit X}}$\\nopagenumbers\\end and <i>R</i> charts with time‐varying parameters","year":2002,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weibull distribution; Control chart; Selection (genetic algorithm); Statistics; \\bar x and R chart; X-bar chart; Variance (accounting); Mathematics; Overline; Chart; Engineering; Process (computing); Control limits; Computer science; Economics","score_opus":0.072865741913077,"score_gpt":0.36142519848104915,"score_spread":0.28855945656797216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529669168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48746398,0.000082458115,0.5099216,0.0009147073,0.0004372075,0.00037120015,0.00031450004,0.0001230786,0.0003712362],"genre_scores_gemma":[0.91783774,0.000027873763,0.081112914,0.00010938426,0.00016969236,0.000058768735,0.000024387364,0.0000331575,0.00062606117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969606,0.00007136566,0.000879491,0.0009858084,0.0007143386,0.00038842394],"domain_scores_gemma":[0.9966765,0.0020680674,0.00023407728,0.0004730219,0.00024260016,0.00030573868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017230248,0.00031473304,0.00046940648,0.00014434173,0.00019696545,0.00033213184,0.00046118192,0.00013361455,0.00013084424],"category_scores_gemma":[0.0018613139,0.00026882422,0.00008643043,0.000111949514,0.0002042436,0.0009577858,0.00010925831,0.00027519505,0.000024798406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034113345,0.00020264623,0.006537935,0.00013848516,0.00009425985,0.0000058528494,0.0012445984,0.9670647,0.0010470212,0.0053606327,0.0003189051,0.017643875],"study_design_scores_gemma":[0.0008275497,0.00010304754,0.0025532618,0.000041343315,0.000019536828,0.000012725106,0.00005629002,0.98678035,0.00022913325,0.0081360545,0.00086861005,0.00037208427],"about_ca_topic_score_codex":0.000040676023,"about_ca_topic_score_gemma":0.0000094153,"teacher_disagreement_score":0.43037376,"about_ca_system_score_codex":0.00016032929,"about_ca_system_score_gemma":0.000030224226,"threshold_uncertainty_score":0.9999764},"labels":[],"label_agreement":null},{"id":"W1539705619","doi":"10.1002/qre.1395","title":"Optimizing the Periodic Inspection Interval for a 1‐out‐of‐2 Cold Standby System Using the Delay‐Time Concept","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Downtime; Interval (graph theory); Reliability engineering; Component (thermodynamics); Process (computing); Inspection time; Point (geometry); Renewal theory; Epoch (astronomy); Computer science; Reliability (semiconductor); Engineering; Mathematics; Statistics; Power (physics); Physics","score_opus":0.019184885988400045,"score_gpt":0.2589261004403154,"score_spread":0.23974121445191535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539705619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24810897,0.0006889443,0.7469855,0.00033174,0.0026330368,0.00063483894,0.000099544784,0.00029499395,0.00022246585],"genre_scores_gemma":[0.99043804,0.00004790555,0.009061237,0.000022767508,0.00029901645,0.00005953676,0.000016413627,0.000024404764,0.00003066053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987797,0.000065806365,0.00051527005,0.00016555497,0.00022300504,0.00025062435],"domain_scores_gemma":[0.9990108,0.00039939448,0.00009622673,0.0002535617,0.00018133532,0.00005869882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013890014,0.00017209786,0.00022991528,0.00004778215,0.00016061058,0.000062868865,0.00020644188,0.000109406705,0.000009516465],"category_scores_gemma":[0.00026971937,0.00011821533,0.00013036579,0.00010126765,0.00013924898,0.00029685817,0.000052215662,0.00020831286,0.000001335226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004162015,0.000038907285,0.00037293482,0.0003527368,0.000100518584,1.1039033e-7,0.0039471984,0.9724081,0.0037640457,0.018628525,0.00012850741,0.00021676111],"study_design_scores_gemma":[0.000320675,0.000027149646,0.0006683219,0.000119082426,0.000040701,0.000009129977,0.0009281197,0.9911269,0.0018886336,0.000033130247,0.004667098,0.00017107102],"about_ca_topic_score_codex":0.000036408484,"about_ca_topic_score_gemma":0.0000018982786,"teacher_disagreement_score":0.7423291,"about_ca_system_score_codex":0.0002935391,"about_ca_system_score_gemma":0.000018704319,"threshold_uncertainty_score":0.48206812},"labels":[],"label_agreement":null},{"id":"W1547408280","doi":"10.1002/qre.1559","title":"Robust Control Charts for Monitoring Process Variability in Phase I Multivariate Individual Observations","year":2013,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"University of Waterloo","keywords":"Multivariate statistics; Control chart; Estimator; Outlier; Covariance; Statistics; Robust statistics; Control limits; Mathematics; Statistical process control; Computer science; Process (computing)","score_opus":0.20191502857322097,"score_gpt":0.44178062778339705,"score_spread":0.23986559921017608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547408280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46539637,0.000011114564,0.5321215,0.0009929058,0.0007561836,0.00047959478,0.00016376871,0.00005342943,0.000025155967],"genre_scores_gemma":[0.9662589,0.0000022521572,0.032971382,0.000036163336,0.00023218384,0.0004161863,0.000014471513,0.00001386291,0.0000546371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99682516,0.0001575377,0.0011631638,0.0006820213,0.0008514494,0.00032064223],"domain_scores_gemma":[0.9908581,0.007505639,0.0002158537,0.00033067187,0.00092577847,0.00016396285],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0054768114,0.00019941316,0.00036741086,0.00016794095,0.00011809055,0.00026275456,0.000511079,0.0001226059,0.000053206157],"category_scores_gemma":[0.026476473,0.00017532703,0.000071341434,0.00029885094,0.00007803361,0.0009935177,0.0000851012,0.0002952668,0.0000075959774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023415335,0.0011308368,0.41040382,0.0003113345,0.00008793485,0.0000022408199,0.0024012364,0.5093645,0.0029869676,0.023812093,0.000059444745,0.04920544],"study_design_scores_gemma":[0.0019408825,0.00004213783,0.49147564,0.000046791454,0.000008644152,0.0000010804724,0.000259074,0.44416705,0.00035454088,0.061226875,0.00022270628,0.00025457834],"about_ca_topic_score_codex":0.000083372244,"about_ca_topic_score_gemma":0.0000017334742,"teacher_disagreement_score":0.5008625,"about_ca_system_score_codex":0.00012879311,"about_ca_system_score_gemma":0.00004918121,"threshold_uncertainty_score":0.9817239},"labels":[],"label_agreement":null},{"id":"W1603749820","doi":"10.1002/qre.1466","title":"Condition‐based Maintenance Optimization Using Neural Network‐based Health Condition Prediction","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Prognostics; Benchmark (surveying); Artificial neural network; Condition-based maintenance; Set (abstract data type); Key (lock); Computer science; Reliability engineering; Data mining; Engineering; Machine learning; Artificial intelligence","score_opus":0.020490239845338716,"score_gpt":0.27601655462931374,"score_spread":0.25552631478397503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603749820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086527385,0.00017967219,0.90869665,0.0007613244,0.002517093,0.0003822098,0.00018611603,0.00056859094,0.00018097064],"genre_scores_gemma":[0.9432925,0.00006604402,0.05488157,0.00036586236,0.00044385044,0.000047480233,0.0008473724,0.00004010644,0.000015204913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813986,0.000104441446,0.00066711265,0.0002853665,0.0003224799,0.00048071705],"domain_scores_gemma":[0.9990742,0.00016390221,0.00013138217,0.00024892925,0.00018313588,0.00019844493],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011817536,0.00025301162,0.00027539025,0.00013115736,0.0001510318,0.00006829852,0.00012067116,0.0001672647,0.0000983094],"category_scores_gemma":[0.00021995077,0.00026943343,0.00009839702,0.00022735473,0.00007853022,0.0006924799,0.00002363327,0.00028079093,0.0000033464905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023562836,0.000068138936,0.005648528,0.00026404342,0.000020455738,2.1847096e-7,0.000057430527,0.9919547,0.000085775,0.0013167973,0.000336787,0.00022358734],"study_design_scores_gemma":[0.0005898057,0.000032983437,0.01912919,0.00013733738,0.000013809871,0.0000061668834,0.000019827025,0.977979,0.00008888279,0.00009518251,0.0016638389,0.00024398131],"about_ca_topic_score_codex":0.000039321538,"about_ca_topic_score_gemma":0.0000018860129,"teacher_disagreement_score":0.85676515,"about_ca_system_score_codex":0.0005119402,"about_ca_system_score_gemma":0.000046346144,"threshold_uncertainty_score":0.9999758},"labels":[],"label_agreement":null},{"id":"W1944080039","doi":"10.1002/qre.1619","title":"Using Fuzzy Cost‐Based FMEA, GRA and Profitability Theory for Minimizing Failures at a Healthcare Diagnosis Service","year":2013,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Profitability index; Maximization; Service (business); Computer science; Fuzzy logic; Reliability engineering; Operations research; Failure mode and effects analysis; Risk analysis (engineering); Operations management; Engineering; Mathematical optimization; Economics; Business; Mathematics; Artificial intelligence","score_opus":0.25734844221832254,"score_gpt":0.45627629831580874,"score_spread":0.1989278560974862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1944080039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9118601,0.00013600812,0.07614661,0.009756009,0.0006066286,0.0011607464,0.00013969334,0.000096434014,0.0000977669],"genre_scores_gemma":[0.9349433,0.00000531267,0.06393199,0.00055483467,0.00009766519,0.00029749225,0.00001223775,0.000028744558,0.00012842855],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966443,0.0003540993,0.0010017103,0.0008370366,0.00084906584,0.00031379142],"domain_scores_gemma":[0.99090827,0.0071760686,0.00025492322,0.00055992685,0.0008744746,0.00022634925],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0053845095,0.00024447468,0.000402518,0.00023870394,0.00021283295,0.0003752615,0.00042993124,0.00016224037,0.00045475966],"category_scores_gemma":[0.011487868,0.00020059351,0.00012168211,0.00025281654,0.00010774653,0.00041860162,0.00031715966,0.0001683671,0.00001939588],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009941182,0.0007246557,0.7865055,0.0018874998,0.00019521742,0.0000053975223,0.004778244,0.04588217,0.011454983,0.061504267,0.0018741598,0.08419376],"study_design_scores_gemma":[0.0014144918,0.000057579582,0.36422518,0.00020709571,0.000020757352,0.000011364968,0.0007098542,0.577576,0.00083881663,0.041271962,0.01310328,0.00056361326],"about_ca_topic_score_codex":0.00054466404,"about_ca_topic_score_gemma":0.00009580972,"teacher_disagreement_score":0.5316938,"about_ca_system_score_codex":0.00019028407,"about_ca_system_score_gemma":0.000048078553,"threshold_uncertainty_score":0.9968388},"labels":[],"label_agreement":null},{"id":"W1973635152","doi":"10.1002/qre.1169","title":"A comparative study of phase II robust multivariate control charts for individual observations","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Estimator; Control chart; Statistics; Multivariate statistics; Mathematics; Robust statistics; Phase (matter); Sample size determination; Covariance; Covariance matrix; Computer science; Process (computing)","score_opus":0.26770427860821777,"score_gpt":0.47641100848483064,"score_spread":0.20870672987661287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973635152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5034152,0.0000013041612,0.49548578,0.0001334639,0.00021018156,0.00036994787,0.00035118603,0.000021518783,0.000011407162],"genre_scores_gemma":[0.67742825,3.112095e-7,0.3223821,0.000011639168,0.000049726637,0.00008540234,0.000014697251,0.0000059231265,0.000021990327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989191,0.000057568734,0.000489082,0.00022103511,0.00019828368,0.00011492442],"domain_scores_gemma":[0.99699146,0.0023611002,0.00013737888,0.0001656087,0.00028288848,0.00006157971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010802515,0.00011659693,0.00031683917,0.000044118176,0.000086231565,0.000014641215,0.00012166669,0.000061414954,0.00001754972],"category_scores_gemma":[0.0032783907,0.00010273027,0.000049718357,0.00004377083,0.000046728637,0.000104655555,0.000047943926,0.00019557416,1.13568404e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000413025,0.0076479716,0.00096875016,0.00028832338,0.0004144911,7.619159e-7,0.010905966,0.02187551,0.018937321,0.93668115,0.00007699447,0.0017897137],"study_design_scores_gemma":[0.012288932,0.0013525113,0.031439126,0.000051053623,0.00016521662,0.0000024529522,0.0009042671,0.75886357,0.0008157365,0.19288564,0.0008029061,0.0004285971],"about_ca_topic_score_codex":0.000026473119,"about_ca_topic_score_gemma":0.000013989288,"teacher_disagreement_score":0.7437955,"about_ca_system_score_codex":0.000012991292,"about_ca_system_score_gemma":0.000015445641,"threshold_uncertainty_score":0.4189219},"labels":[],"label_agreement":null},{"id":"W1974630024","doi":"10.1002/qre.943","title":"Pre‐study analytical method validation: comparison of four alternative approaches based on quality‐level estimation and tolerance intervals","year":2008,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Pesticide Residue Analysis and Safety","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia","keywords":"Tolerance interval; Estimator; Variance (accounting); Statistics; Confidence interval; Quality (philosophy); Context (archaeology); Delta method; Nominal level; Mathematics; Computer science; Reliability engineering; Engineering","score_opus":0.1914602940113335,"score_gpt":0.3700095797474344,"score_spread":0.17854928573610093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974630024","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9600214,0.000016770451,0.03853127,0.0010061777,0.00006120009,0.00016614213,0.00005614065,0.000024709021,0.00011620769],"genre_scores_gemma":[0.994252,0.000007479676,0.0055479505,0.000035380865,0.00006481587,0.0000143864345,0.00004788276,0.0000011504013,0.000028924107],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99823034,0.00027547035,0.00061729306,0.00034559605,0.0004188854,0.00011240826],"domain_scores_gemma":[0.9983072,0.0012278564,0.00017820626,0.000088321875,0.00012814905,0.000070215654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014006087,0.00014214142,0.00036637895,0.000033606968,0.00008901526,0.00002926712,0.00014911778,0.00006846179,0.000040040974],"category_scores_gemma":[0.00080974627,0.00007242497,0.000092503484,0.00013999223,0.00006376843,0.000112196176,0.000053231342,0.00014661794,7.933733e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002356024,0.0011007923,0.54284805,0.000085053216,0.00012140311,0.0000019959612,0.0010341244,0.43107933,0.00106511,0.0037603783,0.000015449496,0.018652724],"study_design_scores_gemma":[0.0001397418,0.000112061294,0.5266176,0.000017520684,0.000011769945,0.0000012483786,0.000074668176,0.4724382,0.00035338505,0.00013240382,0.000023322398,0.00007813027],"about_ca_topic_score_codex":0.0004341367,"about_ca_topic_score_gemma":0.00002658245,"teacher_disagreement_score":0.041358873,"about_ca_system_score_codex":0.000034056444,"about_ca_system_score_gemma":0.000007867051,"threshold_uncertainty_score":0.29534045},"labels":[],"label_agreement":null},{"id":"W1981530540","doi":"10.1002/qre.913","title":"Maintenance contract assessment for aging systems","year":2008,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Failure rate; Reliability engineering; Piecewise; Order (exchange); Function (biology); Time horizon; Computer science; Process (computing); Mathematical optimization; Markov process; Service (business); Total cost; Markov decision process; Operations research; Engineering; Mathematics; Economics; Statistics; Microeconomics","score_opus":0.01670853669979372,"score_gpt":0.2703999733059956,"score_spread":0.25369143660620186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981530540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1976844,0.00019608074,0.7963097,0.0007283485,0.002593489,0.00053449214,0.00009309895,0.000495667,0.0013647309],"genre_scores_gemma":[0.9809666,0.0004574604,0.017906096,0.000046752888,0.00020249595,0.00013759804,0.000054391578,0.00002731253,0.00020123733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874914,0.000023707213,0.00047803752,0.00027416786,0.00021452268,0.00026043746],"domain_scores_gemma":[0.9991471,0.00029744898,0.000051006904,0.00021220067,0.00020386501,0.00008834602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071925117,0.0001810746,0.0002527985,0.00006980044,0.000093950395,0.000051753254,0.00014675244,0.00010947427,0.000013833122],"category_scores_gemma":[0.00033689666,0.00017589869,0.00008985575,0.00007270725,0.000059397116,0.00025967014,0.00002549819,0.00019475413,0.0000023534549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001505678,0.000042082913,0.0031156968,0.00034642257,0.000050543076,0.0000019078275,0.00015568222,0.97473127,0.00072499935,0.019794896,0.00060052495,0.00042094424],"study_design_scores_gemma":[0.0005780063,0.000023778864,0.038042456,0.00006998668,0.000007929759,0.0000265442,0.000034275505,0.94269663,0.00012937869,0.00025776468,0.017882865,0.00025040633],"about_ca_topic_score_codex":0.000048365124,"about_ca_topic_score_gemma":0.0000013606883,"teacher_disagreement_score":0.7832822,"about_ca_system_score_codex":0.00021348079,"about_ca_system_score_gemma":0.000025454481,"threshold_uncertainty_score":0.71729404},"labels":[],"label_agreement":null},{"id":"W1992923086","doi":"10.1002/qre.859","title":"Optimizing the performance of a repairable system under a maintenance and repair contract","year":2007,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reliability engineering; Context (archaeology); Horizon; Computer science; Time horizon; Meaning (existential); Operations research; Risk analysis (engineering); Engineering; Business; Mathematical optimization; Mathematics","score_opus":0.008801323296122262,"score_gpt":0.22705927726403297,"score_spread":0.2182579539679107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992923086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9117765,0.00027736556,0.08561441,0.00024163428,0.00053972116,0.00022194581,0.0000112934285,0.00036711886,0.0009500066],"genre_scores_gemma":[0.989468,0.00029802838,0.010059331,0.000033010034,0.000057619804,0.000012369834,0.0000050217936,0.000015960804,0.00005066952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987688,0.000025532992,0.0005515846,0.00022181166,0.00020737818,0.00022491356],"domain_scores_gemma":[0.99911046,0.00034761234,0.00007300937,0.00025853256,0.00014786846,0.00006248961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022105987,0.00015091448,0.00021716079,0.00006209997,0.000066847184,0.000026640177,0.00012401643,0.00010275369,0.0000061745122],"category_scores_gemma":[0.00023488198,0.00011660493,0.000073536314,0.00011464432,0.00011106443,0.00021037541,0.00004513799,0.00022274772,7.972688e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007928367,0.0000322973,0.004823412,0.0011069123,0.00008036464,0.000001449071,0.0005679225,0.9658861,0.0015068705,0.025274325,0.0000457913,0.00059524295],"study_design_scores_gemma":[0.00032110908,0.000035546786,0.06471093,0.00021092352,0.000014610412,0.000025743178,0.00031648626,0.93213004,0.0009704644,0.000051601794,0.0010482874,0.0001642359],"about_ca_topic_score_codex":0.000064630585,"about_ca_topic_score_gemma":0.0000066950397,"teacher_disagreement_score":0.07769149,"about_ca_system_score_codex":0.00013489553,"about_ca_system_score_gemma":0.000012216115,"threshold_uncertainty_score":0.47550112},"labels":[],"label_agreement":null},{"id":"W2001620004","doi":"10.1002/qre.1298","title":"Ensemble of Surrogates for Dual Response Surface Modeling in Robust Parameter Design","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China; U.S. Department of Energy","keywords":"Kriging; Parametric statistics; Nonparametric statistics; Computer science; Basis (linear algebra); Basis function; Variance (accounting); Radial basis function; Ensemble forecasting; Function (biology); Regression; Machine learning; Mathematical optimization; Econometrics; Mathematics; Statistics; Artificial neural network","score_opus":0.3270333269392578,"score_gpt":0.45037196772559607,"score_spread":0.12333864078633827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001620004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5478636,0.00007824463,0.45137557,0.0001775614,0.0003025724,0.00015482666,0.0000145381755,0.000012799033,0.000020300122],"genre_scores_gemma":[0.6684568,0.000003863709,0.3314224,0.000012632289,0.000025675032,0.000013312169,0.0000019477975,0.0000071466693,0.000056249108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742746,0.0006144884,0.0008334579,0.0003109031,0.00057257415,0.00024110057],"domain_scores_gemma":[0.98828614,0.011015594,0.00011565587,0.00025859213,0.00023198366,0.00009204127],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02066754,0.00013163654,0.00029752328,0.00014894713,0.000029623328,0.00005258147,0.00023783589,0.00010334461,0.00003513718],"category_scores_gemma":[0.017713653,0.00011239126,0.00009599431,0.00018845509,0.00005301399,0.00041181745,0.000089465364,0.000122763,0.0000033833999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091297383,0.00015496313,0.010113903,0.000020506766,0.000012688491,2.2576522e-7,0.0008513802,0.94404364,0.041610077,0.0017886858,0.000020499137,0.00047044197],"study_design_scores_gemma":[0.000415269,0.00007199716,0.015458333,0.000022106478,0.0000035101182,0.0000032374553,0.0002253723,0.9573051,0.023215812,0.00299834,0.00013076686,0.00015021182],"about_ca_topic_score_codex":0.000066097535,"about_ca_topic_score_gemma":0.0000013007783,"teacher_disagreement_score":0.12059321,"about_ca_system_score_codex":0.00008628188,"about_ca_system_score_gemma":0.000030342355,"threshold_uncertainty_score":0.99056053},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.12281908077064864,"score_gpt":0.423784452895995,"score_spread":0.30096537212534635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003842257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1667298,0.000078444464,0.82753026,0.0036890137,0.0011601743,0.00019298868,0.000099309415,0.00010342646,0.0004166102],"genre_scores_gemma":[0.97549516,0.00004011458,0.023092462,0.00013416728,0.0003616396,0.00006186686,0.000010694393,0.0000110819665,0.00079283223],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99800557,0.000022831655,0.0006005565,0.00047181884,0.0006935813,0.0002056358],"domain_scores_gemma":[0.9943851,0.004495161,0.00011118495,0.00024115974,0.00065799785,0.00010934342],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0016044411,0.00013291449,0.0002221979,0.00009332447,0.000095729214,0.00013819906,0.00037528944,0.00007206695,0.000082384475],"category_scores_gemma":[0.032960717,0.000109133485,0.00004731486,0.00024068358,0.000060099006,0.00038021372,0.00008747028,0.00014071768,0.000014635355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019016985,0.0013104582,0.15505353,0.0018592246,0.00024340721,0.000033783457,0.0024763173,0.12801865,0.007591985,0.54416543,0.006026656,0.15131888],"study_design_scores_gemma":[0.0017091385,0.0003017853,0.14593382,0.00022474234,0.000028175044,0.000041285584,0.0005141053,0.06084775,0.0043155574,0.5754462,0.20951906,0.0011183613],"about_ca_topic_score_codex":0.00002891299,"about_ca_topic_score_gemma":0.00000548611,"teacher_disagreement_score":0.80876535,"about_ca_system_score_codex":0.000049368606,"about_ca_system_score_gemma":0.000036517766,"threshold_uncertainty_score":0.97518504},"labels":[],"label_agreement":null},{"id":"W2010277168","doi":"10.1002/qre.1112","title":"Exploring process capability with <i>Mathematica</i>","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Process (computing); Computer science; Software; Software package; Industrial engineering; Software engineering; Engineering drawing; Programming language; Engineering","score_opus":0.1610342907026714,"score_gpt":0.4130972235660644,"score_spread":0.252062932863393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010277168","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8304031,0.0000045093843,0.16675271,0.0008916425,0.00080384826,0.00012196215,0.000021627415,0.00011133112,0.00088925066],"genre_scores_gemma":[0.96495026,0.0000028924655,0.03470497,0.000027426022,0.00017283495,0.000061706465,0.0000025030054,0.000013148403,0.00006426833],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9972257,0.00004219327,0.00068585953,0.0005897581,0.0012273737,0.00022910998],"domain_scores_gemma":[0.9961526,0.002450862,0.00013791161,0.00048791507,0.0005906013,0.00018015907],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002828802,0.0001751811,0.0002742168,0.000097557044,0.00010584576,0.000172459,0.00049585954,0.00006230487,0.00010309216],"category_scores_gemma":[0.016183944,0.00012402641,0.000046483234,0.00026364555,0.00017590643,0.00087114604,0.00009699415,0.0005037433,0.000019291254],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061321247,0.0012633966,0.31160438,0.0012666468,0.00016634125,0.000032509928,0.008992577,0.10359903,0.016989093,0.4817601,0.000087117616,0.0736256],"study_design_scores_gemma":[0.001460954,0.00017622941,0.5088763,0.00017169949,0.000031491658,0.00008857476,0.0018079274,0.11774095,0.010506028,0.34850734,0.009232313,0.0014001954],"about_ca_topic_score_codex":0.000018192215,"about_ca_topic_score_gemma":0.0000091243455,"teacher_disagreement_score":0.19727193,"about_ca_system_score_codex":0.000035449822,"about_ca_system_score_gemma":0.000037174144,"threshold_uncertainty_score":0.99210316},"labels":[],"label_agreement":null},{"id":"W2012649299","doi":"10.1002/qre.463","title":"A sensitivity analysis of an integrated model for joint determination of economic design of $\\overline{x}$‐control charts, economic production quantity and production run length for a deteriorating production system","year":2002,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Control chart; Production (economics); Reliability engineering; Sensitivity (control systems); Engineering; Chart; Process (computing); Statistical process control; Preventive maintenance; Control (management); Operations research; Statistics; Computer science; Mathematics; Economics","score_opus":0.1186407208093857,"score_gpt":0.369464161050311,"score_spread":0.2508234402409253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012649299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45726857,0.000010988394,0.541673,0.00008526443,0.0003702529,0.0004098281,0.0001651642,0.00001645373,4.8997777e-7],"genre_scores_gemma":[0.9005301,0.000008503318,0.09925261,0.0000010507522,0.000108938126,0.000057768873,0.000016745153,0.000009448942,0.000014849548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975722,0.0001701713,0.0012465764,0.0006200582,0.00026718993,0.00012381065],"domain_scores_gemma":[0.9972964,0.0008406427,0.0007865644,0.0002833593,0.00074507226,0.000047949903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005483837,0.00015079128,0.0005679052,0.00036957746,0.00007136699,0.000035197732,0.00009597382,0.00008038523,0.000002032098],"category_scores_gemma":[0.008066973,0.00014043725,0.00010682315,0.00015373179,0.00010138753,0.0006997563,0.00002455965,0.00008104584,1.4026932e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021170102,0.00007791201,0.0015664131,0.00035827185,0.00008808058,4.3312077e-8,0.00045801996,0.9657053,0.018618144,0.001192906,0.0000017872542,0.011721456],"study_design_scores_gemma":[0.00026667299,0.00010822913,0.006045477,0.00005725471,0.00012899369,0.000002906764,0.00020575599,0.9800198,0.012022225,0.0010185495,0.0000022653385,0.000121887206],"about_ca_topic_score_codex":0.00008314867,"about_ca_topic_score_gemma":0.000054831424,"teacher_disagreement_score":0.4432615,"about_ca_system_score_codex":0.00022324402,"about_ca_system_score_gemma":0.00003828123,"threshold_uncertainty_score":0.9657503},"labels":[],"label_agreement":null},{"id":"W2020813649","doi":"10.1002/qre.833","title":"Comparison of Weibull small samples using Monte Carlo simulations","year":2006,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"University of Toronto","keywords":"Sample size determination; Weibull distribution; Monte Carlo method; Type I and type II errors; Reliability (semiconductor); Microelectronics; Sample (material); Reliability engineering; Statistics; Computer science; Mathematics; Engineering","score_opus":0.18035622999293974,"score_gpt":0.4283226680784923,"score_spread":0.24796643808555255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020813649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50347203,0.000010426009,0.49564964,0.00022642812,0.000062511084,0.000080019745,0.00035162433,0.000042931682,0.0001043662],"genre_scores_gemma":[0.92129153,8.266637e-7,0.07853807,0.0000080502305,0.00004419031,0.0000063028974,0.00006766543,0.000006600745,0.00003678434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989763,0.000029426747,0.0005636241,0.0001566279,0.000179149,0.00009488708],"domain_scores_gemma":[0.9986141,0.0008614795,0.00013043042,0.00015066029,0.00020347387,0.00003986193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023240298,0.000092306385,0.00019024832,0.000049023234,0.000058557143,0.000020626429,0.000087950066,0.00005956002,0.00007749231],"category_scores_gemma":[0.0010486316,0.000093062525,0.000050034545,0.000088143446,0.000059785063,0.000049359907,0.000031523712,0.00009143698,0.0000012520665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068482054,0.00023960257,0.016807545,0.00010933034,0.000016384496,8.4129226e-8,0.000072604744,0.17570488,0.00152628,0.80530334,0.00009987564,0.00011321855],"study_design_scores_gemma":[0.00022373223,0.000008838139,0.1257011,0.000030767256,0.000022969223,0.0000012256351,0.000038306003,0.83206743,0.00081407063,0.040139843,0.0008246336,0.00012706258],"about_ca_topic_score_codex":0.00032937987,"about_ca_topic_score_gemma":0.000019730576,"teacher_disagreement_score":0.7651635,"about_ca_system_score_codex":0.000060170354,"about_ca_system_score_gemma":0.000016349057,"threshold_uncertainty_score":0.37949798},"labels":[],"label_agreement":null},{"id":"W2032493365","doi":"10.1002/qre.1750","title":"One‐sided Control Charts Based on Precedence and Weighted Precedence Statistics","year":2014,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Control chart; Statistics; Statistic; Statistical process control; Chart; Computer science; Control limits; Control (management); Constant false alarm rate; Process (computing); Mathematics; Algorithm; Artificial intelligence","score_opus":0.05644773445526943,"score_gpt":0.3792200296446645,"score_spread":0.3227722951893951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032493365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0590078,0.000014200379,0.9382214,0.0012985136,0.00062113255,0.00015707072,0.00017847105,0.00007341224,0.00042797963],"genre_scores_gemma":[0.9526314,0.000008047256,0.04692658,0.00016812609,0.00012960986,0.000021184209,0.0000084586145,0.0000103873435,0.00009617613],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720144,0.00019096363,0.0006543957,0.0005975641,0.0011392743,0.00021633587],"domain_scores_gemma":[0.9883767,0.010509083,0.00016243302,0.00034102917,0.0004255178,0.00018526384],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0026975733,0.00016959185,0.00029381135,0.0001174981,0.0001121133,0.0001848039,0.00032418803,0.00008548656,0.000073831725],"category_scores_gemma":[0.033241857,0.00015032664,0.000031116768,0.0001332069,0.00014185476,0.00024790602,0.000070682014,0.0002674335,0.000016425747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007849026,0.0005911386,0.07824157,0.00042350852,0.000078408986,0.0000056476206,0.000816754,0.3214286,0.002643644,0.4569821,0.0003416624,0.13766207],"study_design_scores_gemma":[0.00052931893,0.0000927757,0.15606092,0.000073002564,0.0000065679515,0.0000011279816,0.000013415987,0.78321856,0.0002873012,0.05870028,0.000824638,0.0001921202],"about_ca_topic_score_codex":0.000021391554,"about_ca_topic_score_gemma":0.0000034544812,"teacher_disagreement_score":0.89362365,"about_ca_system_score_codex":0.00006380499,"about_ca_system_score_gemma":0.000026113185,"threshold_uncertainty_score":0.97490156},"labels":[],"label_agreement":null},{"id":"W2037804320","doi":"10.1002/1099-1638(200007/08)16:4<281::aid-qre338>3.0.co;2-n","title":"More on the mis-specification of the shape parameter with Weibull-to-exponential transformation","year":2000,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta; University of Guelph","keywords":"Weibull distribution; Exponential function; Transformation (genetics); Shape parameter; Exponential distribution; Reliability (semiconductor); Percentile; Scale parameter; Mathematics; Statistics; Weibull modulus; Exponentiated Weibull distribution; Gamma distribution; Power transform; Applied mathematics; Reliability engineering; Power (physics); Engineering; Mathematical analysis; Physics; Discrete mathematics","score_opus":0.0501426559654072,"score_gpt":0.3289302865524668,"score_spread":0.27878763058705963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037804320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7208079,0.0000014251106,0.26337653,0.014221794,0.00006128054,0.0003545436,0.00015262708,0.00003652056,0.0009873495],"genre_scores_gemma":[0.9953456,0.0000025999616,0.004222428,0.00022739693,0.00002787103,0.000048328682,0.00002760572,0.000005950452,0.00009222912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990752,0.000037419224,0.0003415829,0.00013548226,0.00032656195,0.00008377156],"domain_scores_gemma":[0.99876577,0.00078877376,0.00006145007,0.00023308907,0.00011069441,0.000040248426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045329792,0.00008555094,0.000090721616,0.000024976967,0.00007874798,0.00002384486,0.00016224425,0.00004243636,0.00053455634],"category_scores_gemma":[0.00055468996,0.00004891028,0.000044911216,0.00013338306,0.000077542616,0.000048199312,0.000007770093,0.000124207,0.000012173005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008812174,0.0001594702,0.00014129058,0.000050629285,0.000023227984,5.2051817e-8,0.00077075715,0.003678228,0.00039632517,0.9903379,0.00030808998,0.0040459037],"study_design_scores_gemma":[0.0010895393,0.00013582509,0.6159549,0.00025604558,0.0000702572,0.000011682317,0.0005242823,0.31076264,0.008077672,0.035897788,0.02673123,0.00048817278],"about_ca_topic_score_codex":0.000010001262,"about_ca_topic_score_gemma":0.000001278981,"teacher_disagreement_score":0.9544401,"about_ca_system_score_codex":0.00003812642,"about_ca_system_score_gemma":0.000010195307,"threshold_uncertainty_score":0.5853017},"labels":[],"label_agreement":null},{"id":"W2041218528","doi":"10.1002/qre.1147","title":"FaBSR: a method for cluster failure prediction based on Bayesian serial revision and an application to LANL cluster","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cluster (spacecraft); Failure rate; Bayesian probability; Scale (ratio); Computer science; Series (stratigraphy); Population; Statistics; Reliability engineering; Mathematics; Engineering; Artificial intelligence","score_opus":0.008304275900135313,"score_gpt":0.3000407202795615,"score_spread":0.2917364443794262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041218528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12174185,0.000002032344,0.8710824,0.005251456,0.0010192565,0.00066327775,0.000042160464,0.00017532115,0.00002219671],"genre_scores_gemma":[0.66272414,0.0000012878055,0.3361907,0.0004949515,0.0004059143,0.00011607735,0.000040748673,0.000010046278,0.000016166705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984387,0.000089552894,0.00040218927,0.0006103073,0.00028679703,0.00017243781],"domain_scores_gemma":[0.99862146,0.00041778138,0.00007554128,0.00053628505,0.00019125675,0.00015767489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00243761,0.00016639066,0.00019111185,0.000116994044,0.000110312365,0.00014950779,0.00030928082,0.00018938615,0.0000052614414],"category_scores_gemma":[0.0005073299,0.00014061714,0.00005998957,0.00011302282,0.00002305554,0.00041478185,0.000096593714,0.0002297846,0.000002829908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021243556,0.001578436,0.07472172,0.0036589503,0.00014950473,0.0000014540323,0.0065200655,0.5240131,0.03279896,0.084116854,0.002895351,0.26742125],"study_design_scores_gemma":[0.0006334729,0.0001701158,0.022556042,0.000039965103,0.000005290375,0.00000587079,0.000010049323,0.9615073,0.0003672966,0.0005766632,0.0139559405,0.00017199981],"about_ca_topic_score_codex":0.000049239694,"about_ca_topic_score_gemma":0.000021536742,"teacher_disagreement_score":0.54098225,"about_ca_system_score_codex":0.00005342373,"about_ca_system_score_gemma":0.000028923972,"threshold_uncertainty_score":0.57342005},"labels":[],"label_agreement":null},{"id":"W2042937152","doi":"10.1002/qre.1114","title":"Artificial neural network application of modeling failure rate for Boeing 737 tires","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Weibull distribution; Artificial neural network; Failure rate; Reliability (semiconductor); Engineering; Computer science; Reliability engineering; Artificial intelligence; Statistics; Mathematics","score_opus":0.01135295607308184,"score_gpt":0.24936207230611585,"score_spread":0.238009116233034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042937152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45727327,0.000017295208,0.5410807,0.0003792412,0.0008015942,0.00022890627,0.000024950692,0.00013951109,0.000054522257],"genre_scores_gemma":[0.9742137,0.000018386947,0.0252187,0.00001906868,0.00036148902,0.00007195419,0.000066224056,0.000022018321,0.000008431927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888855,0.000017622786,0.00052454893,0.00023649717,0.00013328997,0.0001995128],"domain_scores_gemma":[0.9992645,0.00021482773,0.000059641705,0.00021506613,0.0001886178,0.000057352354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009237388,0.00014720752,0.00020328446,0.00005626118,0.00005824566,0.000037498154,0.00014636944,0.00015010868,0.000009138777],"category_scores_gemma":[0.0004513102,0.00015061181,0.00008744357,0.00009851436,0.00004230929,0.00019840599,0.000029601091,0.00024925577,8.8064803e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025521778,0.000019988936,0.000514504,0.00018951332,0.000018555496,5.5974905e-8,0.00008058751,0.96626085,0.014540665,0.015844312,0.000030844065,0.0024745932],"study_design_scores_gemma":[0.00013118265,0.000012142016,0.0013082189,0.000020154823,0.000010132952,0.0000013139016,0.000018377272,0.9915488,0.0012681611,0.004425154,0.0010991221,0.00015728845],"about_ca_topic_score_codex":0.000042372685,"about_ca_topic_score_gemma":0.000037356953,"teacher_disagreement_score":0.5169405,"about_ca_system_score_codex":0.00003304044,"about_ca_system_score_gemma":0.000011315059,"threshold_uncertainty_score":0.6141771},"labels":[],"label_agreement":null},{"id":"W2047416222","doi":"10.1002/qre.816","title":"An optimal burn‐in preventive‐replacement model associated with a mixture distribution","year":2007,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Mean time between failures; Burn-in; Preventive maintenance; Reliability engineering; Failure rate; Hazard; Population; Computer science; Operations research; Engineering; Environmental health; Medicine","score_opus":0.007036526222194247,"score_gpt":0.24777187613956475,"score_spread":0.2407353499173705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047416222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5217967,0.000019014527,0.4774992,0.000081415485,0.00012448363,0.0001451251,0.000046808353,0.0001362623,0.00015102455],"genre_scores_gemma":[0.99210256,0.000036526133,0.0073718615,0.000017793836,0.000037298716,0.00002288093,0.00034171474,0.000018266823,0.00005108959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998719,0.000030433575,0.0004222496,0.0002907986,0.00026594853,0.0002715649],"domain_scores_gemma":[0.99942595,0.00010963748,0.00004667767,0.0001926403,0.00012837097,0.00009672872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001485067,0.00018068992,0.00018683748,0.000074888376,0.000035912562,0.000044185697,0.0001257091,0.00015317103,0.00001827156],"category_scores_gemma":[0.0002632032,0.00016878909,0.000040968804,0.00015855202,0.000042809846,0.00033745158,0.000020766836,0.00027145972,7.9826924e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000123312,0.00015014203,0.0033064808,0.00005361979,0.000028271925,0.0000017254705,0.00030630617,0.9936327,0.00043627238,0.001606531,0.000023372093,0.00033131836],"study_design_scores_gemma":[0.00056810613,0.00006299398,0.06484281,0.00008545357,0.000008142116,0.000002486953,0.00006637414,0.9331974,0.0005419995,0.00020885967,0.00019735689,0.00021801646],"about_ca_topic_score_codex":0.000017449873,"about_ca_topic_score_gemma":0.000034329503,"teacher_disagreement_score":0.4703059,"about_ca_system_score_codex":0.0003910957,"about_ca_system_score_gemma":0.000017366565,"threshold_uncertainty_score":0.6883019},"labels":[],"label_agreement":null},{"id":"W2064101451","doi":"10.1002/qre.721","title":"Optimal Mean and Tolerance Allocation Using Conformance‐based Design","year":2005,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Reliability engineering; Computer science; Mathematical optimization; Process capability index; Limit (mathematics); Process capability; Engineering; Work in process; Mathematics; Operations management","score_opus":0.17420414038011733,"score_gpt":0.4444025892382796,"score_spread":0.27019844885816224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064101451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4511818,0.000081633356,0.54769135,0.0005768166,0.00021496836,0.00010677973,0.0000062822205,0.000034438384,0.00010591818],"genre_scores_gemma":[0.59503746,0.0000065976465,0.40469068,0.0001280913,0.000073264375,0.0000060955026,0.0000021208273,0.000005473849,0.000050213872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801254,0.00017752708,0.0005961629,0.00039293565,0.0006639262,0.00015687667],"domain_scores_gemma":[0.9983187,0.0010031168,0.00012277807,0.00024152527,0.00021095402,0.00010297911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004546223,0.00014091929,0.00020490788,0.00013015224,0.00008092444,0.0001864431,0.0002501479,0.00008031144,0.00008912438],"category_scores_gemma":[0.0013742807,0.00012280536,0.000048023452,0.00014666417,0.00010088484,0.0005397538,0.000072352195,0.00013358366,0.00000886507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076335964,0.0000472653,0.0010168519,0.000011460769,0.00000851902,4.0496644e-7,0.0004107941,0.9783127,0.012123863,0.002079681,0.000028434526,0.005883719],"study_design_scores_gemma":[0.00034395454,0.00003368528,0.0095286025,0.000017981474,0.0000035787714,0.000006493743,0.000072122246,0.97709185,0.010295707,0.00021202196,0.0022401393,0.00015385503],"about_ca_topic_score_codex":0.000027150347,"about_ca_topic_score_gemma":8.876581e-7,"teacher_disagreement_score":0.14385565,"about_ca_system_score_codex":0.000105778534,"about_ca_system_score_gemma":0.000038947335,"threshold_uncertainty_score":0.5007857},"labels":[],"label_agreement":null},{"id":"W2069764217","doi":"10.1002/qre.437","title":"Optimal control of a deteriorating process with a quadratic loss function","year":2001,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"King Abdulaziz City for Science and Technology","keywords":"Quadratic equation; Mathematical optimization; Function (biology); Process (computing); Point (geometry); Production (economics); Computer science; Bellman equation; Optimal control; Value (mathematics); Mathematics; Statistics; Economics","score_opus":0.04464930346985673,"score_gpt":0.3857687233729716,"score_spread":0.34111941990311484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069764217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4762053,0.000014513689,0.52327484,0.00019130709,0.00013856938,0.00006581373,0.000010568394,0.000024087733,0.00007500594],"genre_scores_gemma":[0.9869741,0.000002804503,0.012841972,0.000023236662,0.000083016384,0.000019002357,0.000002289615,0.000007987762,0.000045557266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978943,0.00005938803,0.00067473843,0.00035211554,0.0008674354,0.0001520606],"domain_scores_gemma":[0.99758935,0.0013225629,0.00021482748,0.00019630823,0.0005967395,0.000080196405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014639095,0.00012406193,0.00025835197,0.000095532785,0.00005947653,0.00007837397,0.00022482677,0.000052569296,0.000048996284],"category_scores_gemma":[0.005144666,0.00009089351,0.000037628226,0.0002491202,0.000105431354,0.00040503842,0.000032984346,0.0001590156,0.0000033566287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092003256,0.00015609025,0.19895619,0.00017407937,0.000059752143,0.000011335093,0.00080942654,0.77825344,0.0017644728,0.008667853,0.000003480578,0.010223843],"study_design_scores_gemma":[0.0018573831,0.00044939062,0.2463109,0.00018042333,0.000033732664,0.00005441713,0.0009846062,0.7295103,0.0006780069,0.0187648,0.00073317345,0.00044285692],"about_ca_topic_score_codex":0.000016099157,"about_ca_topic_score_gemma":0.0000030750944,"teacher_disagreement_score":0.51076883,"about_ca_system_score_codex":0.000039904913,"about_ca_system_score_gemma":0.000034580025,"threshold_uncertainty_score":0.61590177},"labels":[],"label_agreement":null},{"id":"W2080665158","doi":"10.1002/qre.783","title":"Set theoretic formulation of performance reliability of multiple response time‐variant systems due to degradations in system components","year":2006,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Warranty; Monte Carlo method; Component (thermodynamics); Computer science; Quality (philosophy); Set (abstract data type); Degradation (telecommunications); Limit (mathematics); Importance sampling; Function (biology); Mathematical optimization; Power (physics); Engineering; Mathematics; Statistics","score_opus":0.044557318347171776,"score_gpt":0.2992332284278251,"score_spread":0.2546759100806534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080665158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8009725,0.00001681656,0.19790265,0.00014938376,0.00035949209,0.00033753607,0.00013267386,0.000039807237,0.000089106274],"genre_scores_gemma":[0.99491024,0.0000013757647,0.004899298,0.0000024854596,0.000032610245,0.000027317736,0.000029345543,0.000009487706,0.00008785609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967233,0.00033470988,0.0015378699,0.00039354936,0.0008312229,0.00017934003],"domain_scores_gemma":[0.9951787,0.0034986604,0.00025650815,0.00049597287,0.00050009845,0.000070054724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008450209,0.00015934432,0.00045522413,0.0003629392,0.000043053635,0.00004016667,0.0003902867,0.00011546364,0.000012543086],"category_scores_gemma":[0.006324092,0.00013030926,0.00007942358,0.00044627427,0.00007036227,0.0002132546,0.000098830766,0.0001337451,0.0000084725325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034424767,0.00009486431,0.03064057,0.0002640168,0.000007582243,0.000001457813,0.00025309724,0.9453681,0.0035336434,0.019421112,0.000019057863,0.000052261606],"study_design_scores_gemma":[0.00028986906,0.000040263185,0.385406,0.0001466069,0.0000045390443,0.0000080514865,0.00006388103,0.61290836,0.00042485804,0.0004876772,0.00011808422,0.00010180981],"about_ca_topic_score_codex":0.00035546333,"about_ca_topic_score_gemma":0.0000044538097,"teacher_disagreement_score":0.35476542,"about_ca_system_score_codex":0.00017812975,"about_ca_system_score_gemma":0.00004886415,"threshold_uncertainty_score":0.75709856},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.09185004554668968,"score_gpt":0.397363065768271,"score_spread":0.30551302022158133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084870082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48181453,0.000048733178,0.5155466,0.0017026828,0.00031227592,0.00008734083,0.000044781103,0.00006268581,0.00038036617],"genre_scores_gemma":[0.94902384,0.000016133625,0.050588384,0.00012578517,0.00010672221,0.0000064449528,0.0000037305767,0.000008306881,0.00012064378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980553,0.000025404499,0.00048968784,0.0004864061,0.0007630481,0.00018014785],"domain_scores_gemma":[0.99855465,0.0007179203,0.00010779517,0.00023022773,0.00024131824,0.00014810333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013794202,0.00014471124,0.00022147912,0.00008452061,0.00008875145,0.00016744294,0.00018389054,0.000044730357,0.00003645177],"category_scores_gemma":[0.0032107544,0.00010803981,0.000029763596,0.00013924815,0.00013094362,0.00035657018,0.00009185841,0.00021397261,0.000007821],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023663863,0.0002025863,0.07043178,0.00023320586,0.00006938968,0.000034063323,0.0017203353,0.6174111,0.0030828863,0.28369847,0.00003234937,0.022847226],"study_design_scores_gemma":[0.0043608276,0.00028875613,0.6071854,0.00030324998,0.000024556648,0.000101231446,0.0006699532,0.041962676,0.0046862373,0.32186937,0.017326051,0.0012216748],"about_ca_topic_score_codex":0.00011650905,"about_ca_topic_score_gemma":0.0000094995685,"teacher_disagreement_score":0.5754484,"about_ca_system_score_codex":0.000056018678,"about_ca_system_score_gemma":0.000027075806,"threshold_uncertainty_score":0.44057354},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Library science; Smiley; History; Operations research; Engineering; Computer science","score_opus":0.061897039465027455,"score_gpt":0.41268211332249244,"score_spread":0.350785073857465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092433731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8662684,0.00011010006,0.12216121,0.00044514533,0.0010594644,0.0001833733,0.00007841839,0.00007895859,0.009614908],"genre_scores_gemma":[0.9898089,0.0000906489,0.01000364,0.000020135469,0.000029811,0.000012099309,0.0000017572399,0.0000065142804,0.000026471676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99747044,0.000112492206,0.000793237,0.0004423643,0.0010168615,0.00016458098],"domain_scores_gemma":[0.9967431,0.002093061,0.00016557844,0.00032504686,0.00055942545,0.000113825095],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0028311561,0.00012495164,0.0002598579,0.0019164408,0.00004915917,0.000054874763,0.00032487692,0.00007026752,0.00015817319],"category_scores_gemma":[0.019035714,0.00010088842,0.0000921137,0.0038766668,0.00016208402,0.00025911295,0.00004222503,0.00017517195,0.0000033937254],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012706674,0.0004965296,0.7688305,0.0004783836,0.000086388136,0.0000030136846,0.0007454153,0.078363985,0.004191476,0.14066947,0.00053251465,0.0054752687],"study_design_scores_gemma":[0.0006594344,0.000081536586,0.6710668,0.00006035953,0.000013672176,0.000008138989,0.00073558325,0.009376484,0.0046438226,0.2968465,0.016059332,0.00044833217],"about_ca_topic_score_codex":0.000023104338,"about_ca_topic_score_gemma":0.0000013615986,"teacher_disagreement_score":0.15617704,"about_ca_system_score_codex":0.000012337631,"about_ca_system_score_gemma":0.000019178558,"threshold_uncertainty_score":0.98922735},"labels":[],"label_agreement":null},{"id":"W2107636880","doi":"10.1002/qre.1084","title":"Reliability analysis of maintenance data for complex medical devices","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Censoring (clinical trials); Reliability engineering; Reliability (semiconductor); Computer science; Preventive maintenance; Data mining; Engineering; Statistics; Mathematics","score_opus":0.150404268543627,"score_gpt":0.49405158205839544,"score_spread":0.3436473135147684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107636880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91012686,0.00003376826,0.056197576,0.025202272,0.0027377277,0.0008359973,0.0040724445,0.00014909479,0.00064423174],"genre_scores_gemma":[0.9823561,0.00004907287,0.014978177,0.0008385758,0.00032398643,0.00006794708,0.0012720572,0.000013101932,0.00010098023],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99664956,0.0003216668,0.0014655925,0.0005901408,0.0006245105,0.00034853778],"domain_scores_gemma":[0.99192613,0.005525667,0.00033607028,0.001205346,0.00076789927,0.00023885944],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009560827,0.0001675517,0.0005944013,0.00015727902,0.00023047705,0.000009646228,0.0008625795,0.0004138719,0.00087935844],"category_scores_gemma":[0.016700573,0.0001449191,0.00015349255,0.00031388304,0.00019942623,0.00019945468,0.00039698675,0.00091830955,0.0000046042474],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066903,0.00067287503,0.72260106,0.006985028,0.0013256659,0.000001884671,0.003028437,0.0042958963,0.0012747592,0.2515462,0.0033393202,0.004259848],"study_design_scores_gemma":[0.00044105723,0.000020951296,0.6886152,0.00007457931,0.00009906344,6.250871e-7,0.000282759,0.24911484,0.000004686653,0.0010890889,0.06010808,0.00014906368],"about_ca_topic_score_codex":0.0013320034,"about_ca_topic_score_gemma":0.001170243,"teacher_disagreement_score":0.2504571,"about_ca_system_score_codex":0.00006996902,"about_ca_system_score_gemma":0.00024459342,"threshold_uncertainty_score":0.99158216},"labels":[],"label_agreement":null},{"id":"W2115276375","doi":"10.1002/qre.1591","title":"Using Genetic Algorithms to Design Experiments: A Review","year":2014,"lang":"en","type":"review","venue":"Quality and Reliability Engineering International","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Queen's University","funders":"","keywords":"Computer science; Implementation; Set (abstract data type); Optimal design; Range (aeronautics); Genetic algorithm; Mathematical optimization; Machine learning; Engineering; Mathematics; Software engineering","score_opus":0.11815220724140803,"score_gpt":0.41623084700981183,"score_spread":0.2980786397684038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115276375","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.987796e-8,0.45652398,0.54206586,0.00007554174,0.00062386814,0.00057908066,0.000010739315,0.00010260504,0.0000182404],"genre_scores_gemma":[1.8131398e-7,0.5068109,0.49275565,0.00015852341,0.0000993008,0.00010666061,0.000009791893,0.000022033963,0.000036927926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99685645,0.00033885374,0.0010716782,0.0009520215,0.00048595967,0.00029502067],"domain_scores_gemma":[0.9979508,0.0004698167,0.00031704296,0.0007653002,0.00027775136,0.00021930883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011695191,0.00046586734,0.0011980224,0.00023328043,0.000074861426,0.00013227016,0.0010070086,0.00018348635,0.000021219974],"category_scores_gemma":[0.0011576775,0.00042733506,0.00025367422,0.00042453883,0.00003333052,0.00027090596,0.0004893041,0.0003217106,0.000028207814],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023775413,0.00012343492,0.0000011500226,0.018307462,0.0001633517,0.000008579585,0.00012462198,0.1298298,0.0000021769765,0.002229371,0.00012939192,0.8490783],"study_design_scores_gemma":[0.000134977,0.000033817243,0.00000812688,0.012597709,0.000061614504,0.00006340212,0.0000012730721,0.34857193,0.000003167876,0.00008439079,0.6378406,0.00059898075],"about_ca_topic_score_codex":0.000014141201,"about_ca_topic_score_gemma":9.466318e-8,"teacher_disagreement_score":0.84847933,"about_ca_system_score_codex":0.00043648577,"about_ca_system_score_gemma":0.00013028979,"threshold_uncertainty_score":0.99981785},"labels":[],"label_agreement":null},{"id":"W2116776975","doi":"10.1002/qre.1252","title":"A New Chart for Monitoring Service Process Mean","year":2011,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Science Council","keywords":"X-bar chart; Control chart; Chart; Shewhart individuals control chart; EWMA chart; Statistic; \\bar x and R chart; Statistics; Control limits; Computer science; Process (computing); Mathematics","score_opus":0.19637999231399028,"score_gpt":0.4440026417687204,"score_spread":0.24762264945473011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116776975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1587249,0.000036832087,0.8378892,0.000707986,0.001717029,0.0001947072,0.000036668847,0.00011145942,0.0005811885],"genre_scores_gemma":[0.8876512,0.000004236929,0.11153188,0.00004036586,0.00043003677,0.000035531106,0.0000031678906,0.000013727666,0.00028985573],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979544,0.000026511936,0.0006104678,0.0004879074,0.0007064004,0.0002142673],"domain_scores_gemma":[0.9977654,0.001006392,0.00012560564,0.00026494262,0.00065369945,0.0001839638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015710022,0.00014542082,0.00021686342,0.00009675959,0.00008282151,0.00009078435,0.0004994038,0.000075013566,0.000084878244],"category_scores_gemma":[0.005665806,0.00012461346,0.000056074445,0.0002172822,0.000024512206,0.0004485952,0.00008887637,0.00015946967,0.00001571907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020477863,0.00082573574,0.35851675,0.0018576923,0.00035637667,0.000011792394,0.04631772,0.061437815,0.0059332275,0.26978734,0.0008335981,0.25207415],"study_design_scores_gemma":[0.0017981081,0.00018726209,0.34569648,0.00022273672,0.000034060857,0.0000143392435,0.0023535476,0.09100647,0.014614411,0.5316661,0.011364529,0.0010419351],"about_ca_topic_score_codex":0.000083202794,"about_ca_topic_score_gemma":0.0000042151805,"teacher_disagreement_score":0.7289263,"about_ca_system_score_codex":0.000055228884,"about_ca_system_score_gemma":0.00004452494,"threshold_uncertainty_score":0.67829084},"labels":[],"label_agreement":null},{"id":"W2117915076","doi":"10.1002/qre.912","title":"A process capability/customer satisfaction approach to short‐run processes","year":2008,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Customer satisfaction; Statistical process control; Computer science; Process (computing); Perspective (graphical); Control (management); Sample (material); Class (philosophy); Industrial engineering; Operations research; Engineering; Artificial intelligence; Marketing; Business","score_opus":0.10863656321507738,"score_gpt":0.41007378026695007,"score_spread":0.3014372170518727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117915076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65081793,0.000029464201,0.3460521,0.00045566986,0.0005231693,0.0002499793,0.000044828517,0.00013756932,0.0016892445],"genre_scores_gemma":[0.97934127,0.000012500322,0.020084823,0.00006988092,0.00019828598,0.00009140901,0.0000074778036,0.000016200662,0.00017815862],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963979,0.00007673639,0.00085910695,0.0008452958,0.0015351429,0.0002858172],"domain_scores_gemma":[0.9967943,0.0014325937,0.00009490774,0.0003800616,0.0010451586,0.000252979],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0017251412,0.00021989891,0.00033983355,0.00021515586,0.00019336294,0.000101803234,0.0004406565,0.00010799777,0.00004273351],"category_scores_gemma":[0.022507494,0.00018346422,0.000059327107,0.00068892975,0.00013647557,0.0006053276,0.00012137244,0.00030444615,0.00003885417],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003020664,0.00065662316,0.5029629,0.00088407926,0.000075583244,0.0000093643375,0.0072721164,0.45415634,0.0012279557,0.011273335,0.00046606665,0.020713568],"study_design_scores_gemma":[0.00038308874,0.000076538825,0.94413203,0.000056742287,0.000012132307,0.00007401882,0.0007332501,0.032233015,0.0013721696,0.01508794,0.005115851,0.000723195],"about_ca_topic_score_codex":0.0000690037,"about_ca_topic_score_gemma":0.000006603005,"teacher_disagreement_score":0.44116914,"about_ca_system_score_codex":0.00015147818,"about_ca_system_score_gemma":0.00009716446,"threshold_uncertainty_score":0.98572636},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.09785959620950711,"score_gpt":0.447518173055188,"score_spread":0.34965857684568086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124599093","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93841004,0.00006244379,0.053484183,0.0069904877,0.0005619775,0.00024949582,0.000024963912,0.000034606514,0.00018181019],"genre_scores_gemma":[0.99722606,0.0000131941615,0.0023716893,0.000118004435,0.00020171038,0.0000315942,0.0000016160192,0.000004069552,0.00003203827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985428,0.000044023913,0.0003243515,0.00034345145,0.00059323385,0.00015213304],"domain_scores_gemma":[0.996981,0.0025923995,0.000065473665,0.00016484685,0.00014783308,0.000048393515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023934091,0.00010451441,0.00015080482,0.00009548567,0.000075365846,0.00012425694,0.00027673456,0.000047504847,0.000004184659],"category_scores_gemma":[0.0071067526,0.00007034104,0.000021990869,0.00013143424,0.000036754707,0.00016555248,0.00002253743,0.00016226305,9.980341e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007502532,0.00074294576,0.08476432,0.00045787301,0.000041089166,0.000012564868,0.022324065,0.10434709,0.0023580117,0.45782208,0.00016937009,0.32621032],"study_design_scores_gemma":[0.00072975503,0.0002558454,0.508656,0.00013938016,0.0000054343086,0.0000065024633,0.0011719731,0.04755903,0.001361202,0.43840688,0.0013976201,0.00031035382],"about_ca_topic_score_codex":0.0000048740258,"about_ca_topic_score_gemma":0.0000025675183,"teacher_disagreement_score":0.42389172,"about_ca_system_score_codex":0.00003443506,"about_ca_system_score_gemma":0.000007494617,"threshold_uncertainty_score":0.850796},"labels":[],"label_agreement":null},{"id":"W2130576399","doi":"10.1002/qre.1138","title":"Reliability estimation in a Weibull lifetime distribution with zero‐failure field data","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Reliability (semiconductor); Weibull distribution; Estimator; Reliability engineering; Product (mathematics); Estimation; Statistics; Computer science; Shrinkage; Mathematics; Engineering; Power (physics)","score_opus":0.03119370599580855,"score_gpt":0.3490057674132149,"score_spread":0.3178120614174063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130576399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22286582,0.0000018202546,0.76879877,0.0068574436,0.00013980175,0.00026094122,0.00079597544,0.00012546087,0.0001539433],"genre_scores_gemma":[0.91403615,0.0000036852334,0.08445021,0.00007750675,0.000041014922,0.000051327013,0.0013011282,0.000009677558,0.000029282699],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985005,0.000046781897,0.0005256907,0.00043851577,0.0003166513,0.0001718175],"domain_scores_gemma":[0.9976491,0.0012968181,0.00010788734,0.00065667176,0.00018390306,0.000105628686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011205566,0.0001564736,0.00019676248,0.0000502632,0.00006871872,0.00006691235,0.00030948507,0.00014719664,0.00018243105],"category_scores_gemma":[0.008270409,0.00013766914,0.000027521666,0.0001906428,0.000083978615,0.00028467362,0.000117574986,0.00046100386,0.0000113818905],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001013145,0.0006156579,0.0055619343,0.00027514968,0.000029055407,0.0000022421962,0.00012638359,0.0037981295,0.0006230167,0.9826388,0.0036777568,0.002550597],"study_design_scores_gemma":[0.0009048647,0.000053648426,0.19553553,0.000088612695,0.000029652996,0.000020451214,0.000025628568,0.71352977,0.00031374732,0.07636981,0.012732823,0.00039547676],"about_ca_topic_score_codex":0.00014517181,"about_ca_topic_score_gemma":0.00007409078,"teacher_disagreement_score":0.90626895,"about_ca_system_score_codex":0.00006854209,"about_ca_system_score_gemma":0.000045207806,"threshold_uncertainty_score":0.9901049},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.06312127271983545,"score_gpt":0.41762804580426416,"score_spread":0.3545067730844287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131080154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34061933,0.000016726586,0.65311134,0.0015363235,0.0032406163,0.000099018784,0.000025503801,0.000086187836,0.0012649763],"genre_scores_gemma":[0.9277458,0.000004875003,0.07072873,0.00005012773,0.0004448378,0.0000067035007,0.0000037190987,0.00000987819,0.0010052831],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99755234,0.00003100749,0.00064155494,0.00050794514,0.001056246,0.00021091428],"domain_scores_gemma":[0.99675536,0.0021530774,0.00011748087,0.00040644323,0.00031646734,0.0002511744],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002238862,0.00014476989,0.00023081919,0.00021341516,0.0000736501,0.00019188559,0.00053802173,0.00010564412,0.00033182456],"category_scores_gemma":[0.017586084,0.000118980075,0.00007007647,0.0003977021,0.0000624444,0.00036495907,0.00015594964,0.00049530354,0.000086982436],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025823628,0.0005075169,0.17773366,0.0002036925,0.00013766643,0.000024537836,0.0026750094,0.054599352,0.028635947,0.39109582,0.00541208,0.33871648],"study_design_scores_gemma":[0.00094899477,0.000071123344,0.560011,0.0000354936,0.000013339363,0.000022677099,0.00017789472,0.18137051,0.0028751213,0.16591127,0.08786999,0.0006925274],"about_ca_topic_score_codex":0.000091775095,"about_ca_topic_score_gemma":0.000011369526,"teacher_disagreement_score":0.58712655,"about_ca_system_score_codex":0.000040674004,"about_ca_system_score_gemma":0.000047476842,"threshold_uncertainty_score":0.9906892},"labels":[],"label_agreement":null},{"id":"W2137109403","doi":"10.1002/qre.1790","title":"A Prediction Region‐based Approach to Model Uncertainty for Multi‐response Optimization","year":2015,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Minimax; Mathematical optimization; Function (biology); Set (abstract data type); Computer science; Dispersion (optics); Variable (mathematics); Process (computing); Mathematics","score_opus":0.3965922080803353,"score_gpt":0.46378172639864923,"score_spread":0.06718951831831393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137109403","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024232857,0.000016971684,0.973325,0.0010404581,0.0004918372,0.00049406855,0.00009349446,0.000097612436,0.00020768658],"genre_scores_gemma":[0.28869507,7.839662e-7,0.7105678,0.00018644506,0.000050770057,0.00012844504,0.000026842708,0.000011192221,0.00033269887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975469,0.00030962913,0.0006197232,0.0005571176,0.0007995622,0.00016704483],"domain_scores_gemma":[0.99750495,0.0010980652,0.00010531755,0.00035131493,0.0006986129,0.00024171677],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008487799,0.00015005675,0.0002175367,0.00023348854,0.00006276203,0.00015067802,0.00035509627,0.000113868635,0.000006815584],"category_scores_gemma":[0.0157616,0.00012843934,0.00009630685,0.00026218168,0.000047082816,0.00028208236,0.000083885076,0.00010326059,0.0000034269626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009959625,0.00015535038,0.00030552587,0.000009893459,0.0000074944173,1.2869754e-7,0.0004941424,0.99445873,0.00048264966,0.0022751146,0.0003783671,0.0004366505],"study_design_scores_gemma":[0.00086157775,0.000092667695,0.0010170344,0.0000102264285,0.000004340672,0.0000024137134,0.00018592228,0.99519867,0.0002918588,0.0008046088,0.0013889645,0.00014168976],"about_ca_topic_score_codex":0.000023400515,"about_ca_topic_score_gemma":3.83732e-7,"teacher_disagreement_score":0.2644622,"about_ca_system_score_codex":0.000262678,"about_ca_system_score_gemma":0.000104337145,"threshold_uncertainty_score":0.99252903},"labels":[],"label_agreement":null},{"id":"W2155213674","doi":"10.1002/qre.1861","title":"An Efficient Approximate Markov Chain Method in Dynamic Fault Tree Analysis","year":2015,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Health Services and Policy Research","funders":"","keywords":"Markov chain; Fault tree analysis; Truncation (statistics); Markov model; Computer science; Markov process; Transformation (genetics); Chain (unit); Mathematics; Tree (set theory); Mathematical optimization; Algorithm; Applied mathematics; Reliability engineering; Statistics; Engineering; Combinatorics","score_opus":0.052127340671007105,"score_gpt":0.4113555825427406,"score_spread":0.3592282418717335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155213674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5047756,0.000042855827,0.49253514,0.0018867402,0.00022325714,0.000076219585,0.000036359685,0.000041665207,0.00038217317],"genre_scores_gemma":[0.95911974,0.000020625663,0.04049378,0.0000496819,0.00003578914,0.000012928728,0.0000424185,0.000007239251,0.00021780786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961679,0.0005422804,0.0010241116,0.0007082158,0.0013241126,0.00023338916],"domain_scores_gemma":[0.9976214,0.0009792278,0.00016924269,0.00064194185,0.0003580143,0.00023013484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01458365,0.00017831806,0.0005046209,0.0008415033,0.000048381546,0.00017740764,0.0006287884,0.00012371755,0.000075435564],"category_scores_gemma":[0.00392899,0.00014134902,0.00023624733,0.0015048571,0.00005738127,0.00022432179,0.000115496376,0.00023210091,0.000012237391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060215363,0.00016682757,0.024623033,0.000006062456,0.00009760484,0.0000029594319,0.0009270281,0.95853865,0.0000764785,0.0021164448,0.000013077683,0.013371591],"study_design_scores_gemma":[0.0002923716,0.000019525582,0.17618166,0.000004105071,0.000039347167,0.0000016004573,0.0005038136,0.8182054,0.000017665729,0.00418078,0.00040505256,0.00014868993],"about_ca_topic_score_codex":0.00050149777,"about_ca_topic_score_gemma":0.00030490285,"teacher_disagreement_score":0.45434415,"about_ca_system_score_codex":0.00018647262,"about_ca_system_score_gemma":0.00004129647,"threshold_uncertainty_score":0.57640463},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.08874728958372513,"score_gpt":0.42284835347913136,"score_spread":0.3341010638954062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160885386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013758474,0.00009808375,0.977351,0.0059585245,0.0021959466,0.00029143543,0.0002303413,0.00005536429,0.00006084891],"genre_scores_gemma":[0.9766812,0.000021975698,0.022467524,0.000078087425,0.00046920477,0.00011898731,0.0000021325325,0.000010882754,0.0001500544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997645,0.00013282069,0.0006023623,0.00041940916,0.0009538764,0.00024652103],"domain_scores_gemma":[0.9674602,0.031488936,0.00015769755,0.00044084934,0.0003772552,0.000075049975],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0038841902,0.00014995201,0.00020602667,0.00004499823,0.0006372803,0.00014252205,0.00065053353,0.000045967103,0.000015936745],"category_scores_gemma":[0.02828356,0.00008442844,0.00009235749,0.00013330363,0.00020173717,0.00016100847,0.000046265122,0.00023158184,0.000009703851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037848804,0.000082123974,0.012484916,0.000034147906,0.000036884212,0.0000020401098,0.00024263778,0.88752776,0.0002096641,0.09012526,0.00015281192,0.00872327],"study_design_scores_gemma":[0.0009416823,0.00006652802,0.05619325,0.000044781034,0.000009959711,0.000005203507,0.00010408876,0.866215,0.00032445055,0.048922762,0.026956072,0.00021625325],"about_ca_topic_score_codex":0.00003639842,"about_ca_topic_score_gemma":0.0000014624194,"teacher_disagreement_score":0.9629227,"about_ca_system_score_codex":0.00008072471,"about_ca_system_score_gemma":0.000039858,"threshold_uncertainty_score":0.9799016},"labels":[],"label_agreement":null},{"id":"W2172254927","doi":"10.1002/qre.1418","title":"Maximum Likelihood Estimation for a Hidden Semi‐Markov Model with Multivariate Observations","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multivariate statistics; Maximum likelihood; Statistics; Hidden Markov model; Markov model; Markov chain; Estimation; Mathematics; Multivariate analysis; Econometrics; Estimation theory; Expectation–maximization algorithm; Computer science; Artificial intelligence; Engineering","score_opus":0.021745150294988164,"score_gpt":0.2598320851282756,"score_spread":0.23808693483328744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172254927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15088685,0.000050701132,0.84687626,0.00066074874,0.0004830275,0.00038833494,0.000091971786,0.00032068658,0.00024140658],"genre_scores_gemma":[0.6899443,0.00003747753,0.3094484,0.000049459795,0.00011208981,0.0001676661,0.00015253873,0.0000309842,0.000057072688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882007,0.000018372528,0.0003974632,0.00022653538,0.00020901885,0.0003285262],"domain_scores_gemma":[0.99918133,0.000219859,0.00005447016,0.0002245535,0.00018920943,0.00013060651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006982945,0.00020445736,0.00020104983,0.000079128135,0.00007634221,0.000055380497,0.00012598878,0.0001346729,0.000010862746],"category_scores_gemma":[0.00042293972,0.00018746003,0.000065413595,0.00011303948,0.00003603495,0.00066631084,0.00003032656,0.0001683479,0.000002560168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042824686,0.000082524544,0.0017637009,0.00027015698,0.00005277396,7.533276e-8,0.0004734877,0.987469,0.0008073304,0.0062326314,0.0001502065,0.0026552563],"study_design_scores_gemma":[0.00051765796,0.000022089995,0.016551444,0.000055223572,0.000024004814,0.000003348324,0.00003436054,0.9788168,0.00028975928,0.0026807634,0.00075011404,0.00025439975],"about_ca_topic_score_codex":0.00002558799,"about_ca_topic_score_gemma":0.0000037005477,"teacher_disagreement_score":0.53905743,"about_ca_system_score_codex":0.00016968761,"about_ca_system_score_gemma":0.000023449342,"threshold_uncertainty_score":0.7644398},"labels":[],"label_agreement":null},{"id":"W2215891809","doi":"10.1002/qre.1953","title":"Reliability Analysis Method on Repairable System with Standby Structure Based on Goal Oriented Methodology","year":2015,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"China Scholarship Council","keywords":"Standby power; Operator (biology); Fault tree analysis; Reliability engineering; Reliability (semiconductor); Markov process; Process (computing); Failure mode and effects analysis; Computer science; Engineering; Power (physics); Mathematics; Electrical engineering; Statistics","score_opus":0.08812272769219064,"score_gpt":0.4009745531765538,"score_spread":0.31285182548436313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215891809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19777198,0.000016550619,0.7976925,0.002287499,0.0006910188,0.00018250663,0.00024549247,0.0001539537,0.0009585269],"genre_scores_gemma":[0.86470187,0.0000032021053,0.13466595,0.00016905073,0.00011704067,0.000013709359,0.00006157435,0.000013273996,0.00025435505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931329,0.0018806489,0.0011676967,0.001196725,0.0023203008,0.00030175276],"domain_scores_gemma":[0.99078345,0.0060730428,0.00035874813,0.0012785469,0.0011902521,0.0003159602],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.017956715,0.0003148396,0.0008963607,0.000696733,0.00013485,0.00014056987,0.0005653037,0.00023447155,0.00013766435],"category_scores_gemma":[0.014033376,0.0002059393,0.00035155495,0.0016918101,0.000116653406,0.0002127542,0.0000878377,0.00047139372,0.0000127181165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094192335,0.000109008615,0.018704964,0.00002690232,0.00026960252,0.000004426325,0.0002666108,0.9656069,0.000033892346,0.0129750455,0.00017335062,0.0008873168],"study_design_scores_gemma":[0.0008657686,0.0003122242,0.06840341,0.00003207248,0.00020477836,0.0000051593747,0.00065959804,0.92067707,0.00018615185,0.002800088,0.005548335,0.00030533937],"about_ca_topic_score_codex":0.0005501932,"about_ca_topic_score_gemma":0.00007046824,"teacher_disagreement_score":0.66692984,"about_ca_system_score_codex":0.0004437875,"about_ca_system_score_gemma":0.00012808743,"threshold_uncertainty_score":0.9942718},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.044054915157610744,"score_gpt":0.3629226126963945,"score_spread":0.31886769753878375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280804815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15911955,0.000040644212,0.8356718,0.0035142766,0.000977053,0.00010416558,0.00009819783,0.00009839301,0.00037596587],"genre_scores_gemma":[0.9867015,0.000014459091,0.0120038325,0.00013370742,0.0003227911,0.000019051398,0.0000024774085,0.0000124311555,0.0007897812],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726665,0.00011317753,0.0007708987,0.0005619482,0.001032594,0.0002547469],"domain_scores_gemma":[0.99528706,0.0036532457,0.00014157899,0.0003346993,0.0004192143,0.00016422417],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0023559784,0.00016962536,0.00028069314,0.000124328,0.00009282677,0.00012568802,0.00044159644,0.000088851426,0.0003111524],"category_scores_gemma":[0.010952857,0.0001091243,0.00008256731,0.00014135962,0.00009090132,0.00047913476,0.00010914976,0.00015201957,0.00005997996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086875673,0.0005370755,0.11903921,0.00022506325,0.00031560962,0.00006793107,0.0013352573,0.06047365,0.1546229,0.38516033,0.0012019963,0.27615222],"study_design_scores_gemma":[0.005059295,0.00018187016,0.30815047,0.00026687904,0.00002769361,0.000029596615,0.00009935606,0.35794908,0.0050345664,0.2766152,0.045201045,0.0013849246],"about_ca_topic_score_codex":0.000016801188,"about_ca_topic_score_gemma":0.0000015607461,"teacher_disagreement_score":0.82758194,"about_ca_system_score_codex":0.00011660187,"about_ca_system_score_gemma":0.00002963284,"threshold_uncertainty_score":0.9973783},"labels":[],"label_agreement":null},{"id":"W2410674143","doi":"10.1002/qre.2022","title":"Using Bayesian Variable Selection to Analyze Regular Resolution IV Two‐level Fractional Factorial Designs","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fractional factorial design; Selection (genetic algorithm); Bayesian probability; Mathematics; Factorial experiment; Variable (mathematics); Statistics; Aliasing; Feature selection; Computer science; Artificial intelligence","score_opus":0.21593583600767408,"score_gpt":0.4599711975242283,"score_spread":0.24403536151655425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2410674143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0882054,0.000010008558,0.90842396,0.00070670655,0.0020235295,0.00018418416,0.0000741594,0.000074811534,0.0002972433],"genre_scores_gemma":[0.5112982,0.0000016789463,0.48760262,0.000061604675,0.00051723706,0.000013404456,0.0000046109035,0.000013521183,0.00048714413],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638397,0.00045712758,0.0008371534,0.00071702997,0.0013234222,0.0002812848],"domain_scores_gemma":[0.9971184,0.0016195636,0.00017619466,0.0003337415,0.00053475786,0.00021730037],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0062382095,0.00020410231,0.00028695303,0.00033230294,0.00016408289,0.00019652056,0.00036785653,0.0001491547,0.0006261737],"category_scores_gemma":[0.0088327415,0.00015272002,0.00011012878,0.00051684596,0.00006347709,0.0007133406,0.00013811942,0.0001698019,0.000030810857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005541006,0.0001981158,0.01279453,0.00001384787,0.00009057586,0.0000022211877,0.00026374712,0.22931795,0.6559542,0.09682862,0.0005786059,0.003403497],"study_design_scores_gemma":[0.0019735857,0.00024923528,0.07733469,0.00013787937,0.00003567706,0.00006457731,0.00010192578,0.7922534,0.029638167,0.07275795,0.024493909,0.0009590469],"about_ca_topic_score_codex":0.00030926714,"about_ca_topic_score_gemma":0.000005663172,"teacher_disagreement_score":0.626316,"about_ca_system_score_codex":0.0005896489,"about_ca_system_score_gemma":0.0001006318,"threshold_uncertainty_score":0.9995163},"labels":[],"label_agreement":null},{"id":"W2508662586","doi":"10.1002/qre.2047","title":"Planning and Analyzing Experiments with Models that Distinguish Between Replicates and Repeats","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Variation (astronomy); Binary number; Value (mathematics); Artificial intelligence; Data mining; Mathematics; Machine learning","score_opus":0.24185350200226333,"score_gpt":0.45400472597203956,"score_spread":0.21215122396977623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508662586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79304034,0.00028237526,0.2053119,0.00065194676,0.0000929891,0.00009768897,0.000021504713,0.000052959884,0.00044831572],"genre_scores_gemma":[0.9582521,0.000019332947,0.041455537,0.000025296207,0.00006172563,0.000012924389,0.0000025178683,0.00001099302,0.00015958615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99799937,0.00013214975,0.0004538497,0.00066482177,0.0005775668,0.0001722492],"domain_scores_gemma":[0.9971971,0.0020751546,0.00014171418,0.00032116706,0.00011894246,0.00014593756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029508907,0.00016413901,0.0002783442,0.00011492736,0.00009325008,0.00019843395,0.00021377565,0.00007004079,0.000016475959],"category_scores_gemma":[0.0021124713,0.0001000782,0.00003247869,0.00009753284,0.00016103515,0.00047789188,0.00021853563,0.00009983973,0.0000010710031],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010499459,0.00004398849,0.9548357,0.000030543088,0.00008288611,0.0000055195037,0.0016622212,0.0015877752,0.012794713,0.005667706,0.000038247505,0.023145672],"study_design_scores_gemma":[0.0012134142,0.00017905464,0.9233617,0.00033303007,0.000023393963,0.000034689572,0.0006108823,0.031998605,0.019495463,0.020989968,0.0011382335,0.00062154926],"about_ca_topic_score_codex":0.00004600105,"about_ca_topic_score_gemma":3.8832133e-7,"teacher_disagreement_score":0.16521177,"about_ca_system_score_codex":0.000050169245,"about_ca_system_score_gemma":0.000011236149,"threshold_uncertainty_score":0.40810704},"labels":[],"label_agreement":null},{"id":"W2522463850","doi":"10.1002/qre.2078","title":"On Reliability of a Multi‐Socket Repairable System","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"JDA Software (Canada)","funders":"","keywords":"Component (thermodynamics); Context (archaeology); Dilemma; Reliability (semiconductor); Poisson process; Computer science; Reliability engineering; Process (computing); Sibling; Poisson point process; Point process; Poisson distribution; Operations research; Engineering; Mathematics; Statistics; Economics","score_opus":0.055608521742254065,"score_gpt":0.35883689238644484,"score_spread":0.3032283706441908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522463850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.254245,0.000003486957,0.7424431,0.0014711998,0.00018959459,0.00019529462,0.00051294704,0.00019481081,0.00074456277],"genre_scores_gemma":[0.97427523,0.000003856528,0.025361473,0.000020715352,0.00002414142,0.000042580035,0.000014146429,0.000008301513,0.0002495503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99872756,0.00006320447,0.0005564538,0.00026576506,0.00026950668,0.00011752807],"domain_scores_gemma":[0.997274,0.0019473224,0.00012839616,0.00032604532,0.0002456094,0.0000786739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008917083,0.00011141692,0.00020018595,0.000044416935,0.000043414464,0.000011030519,0.00012695277,0.0000783387,0.000119982586],"category_scores_gemma":[0.006852211,0.00007851544,0.00007429248,0.00007670552,0.00008424551,0.0000704885,0.000039009894,0.000083336774,0.000015946818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028539122,0.00021110947,0.00071961404,0.0002519102,0.000013940925,2.4835782e-7,0.000040621748,0.00024482014,0.0006153835,0.9970918,0.00034853508,0.00043343508],"study_design_scores_gemma":[0.00513341,0.0002807828,0.340699,0.0016470347,0.00009908388,0.000024208004,0.0002668738,0.3072427,0.009474947,0.3257898,0.008158913,0.0011832643],"about_ca_topic_score_codex":0.0000211732,"about_ca_topic_score_gemma":7.618125e-7,"teacher_disagreement_score":0.72003025,"about_ca_system_score_codex":0.00015532371,"about_ca_system_score_gemma":0.000019062882,"threshold_uncertainty_score":0.82032317},"labels":[],"label_agreement":null},{"id":"W2523137375","doi":"10.1002/qre.2088","title":"Modeling Failure Process and Quantifying the Effects of Multiple Types of Preventive Maintenance for a Repairable System","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Connaught Fund; Sharif University of Technology","keywords":"Preventive maintenance; Reliability engineering; Poisson process; Reliability (semiconductor); Corrective maintenance; Planned maintenance; Truck; Function (biology); Poisson distribution; Process (computing); Engineering; Failure rate; Computer science; Statistics; Power (physics); Mathematics; Automotive engineering","score_opus":0.010317740008198345,"score_gpt":0.24497710677062448,"score_spread":0.23465936676242613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523137375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5639292,0.00014642891,0.43505323,0.00015166496,0.0002510571,0.00034474276,0.000031662214,0.000070022674,0.000021966507],"genre_scores_gemma":[0.99432373,0.0001253015,0.0054124407,0.0000028783404,0.000027443924,0.00007138778,0.0000033827057,0.000013344268,0.000020116346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999143,0.000026967546,0.000390857,0.0001833863,0.00012815102,0.00012765171],"domain_scores_gemma":[0.998802,0.0007066498,0.00006634906,0.00015914817,0.00023785965,0.000027999205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006507681,0.000112364614,0.00021444308,0.00004218774,0.000033121945,0.000010533287,0.0001080623,0.000073558724,9.819753e-7],"category_scores_gemma":[0.0015270307,0.000070683986,0.00006236684,0.00006139408,0.000061883155,0.00015774336,0.000026947164,0.000065699234,1.1995797e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090273934,0.000041731066,0.0026871937,0.009662612,0.00011860255,2.0313573e-7,0.00082902634,0.95854837,0.013555994,0.013292818,0.000017704337,0.0011554959],"study_design_scores_gemma":[0.0005244743,0.00003666038,0.0013878911,0.0010251375,0.000019668236,0.0000023870662,0.00018644331,0.9894352,0.006690061,0.00051664177,0.00006892299,0.000106528074],"about_ca_topic_score_codex":0.000026395563,"about_ca_topic_score_gemma":0.000006670676,"teacher_disagreement_score":0.4303945,"about_ca_system_score_codex":0.000050119226,"about_ca_system_score_gemma":0.000011762766,"threshold_uncertainty_score":0.28824094},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.12536523418914738,"score_gpt":0.3847240618444989,"score_spread":0.25935882765535156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530922009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9670176,0.000015798802,0.026034884,0.0062891124,0.00035183816,0.00011311756,0.00001760182,0.000008778565,0.00015129412],"genre_scores_gemma":[0.99940044,0.000014163414,0.00049251755,0.000028446215,0.000029010165,0.000013569946,1.8048875e-7,0.0000031060606,0.000018583338],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9976083,0.000119662705,0.0005305549,0.00018624823,0.0014588729,0.000096374766],"domain_scores_gemma":[0.99404526,0.005224817,0.00012361309,0.00029039534,0.0002910835,0.000024806794],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006431922,0.00006785899,0.0001291746,0.000058890662,0.000022810606,0.000012702843,0.0005340164,0.000027463544,0.00003174721],"category_scores_gemma":[0.020912804,0.000028664796,0.000032028816,0.00014356067,0.00014463431,0.0001369854,0.00005535176,0.000109666216,0.0000025374775],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058998115,0.00073931227,0.30270103,0.0004383908,0.00007725177,0.0000029576518,0.008841777,0.08504177,0.028398497,0.51753587,0.00066844316,0.054964762],"study_design_scores_gemma":[0.0004813472,0.00016324087,0.8866473,0.00046091012,0.00000452027,0.0000017827899,0.0006145951,0.04307973,0.0132225985,0.053611226,0.001530249,0.00018250872],"about_ca_topic_score_codex":0.000015172173,"about_ca_topic_score_gemma":0.0000045833826,"teacher_disagreement_score":0.5839463,"about_ca_system_score_codex":0.000040585946,"about_ca_system_score_gemma":0.00002032536,"threshold_uncertainty_score":0.9873345},"labels":[],"label_agreement":null},{"id":"W2589626016","doi":"10.1002/qre.2142","title":"Pattern‐based prognostic methodology for condition‐based maintenance using selected and weighted survival curves","year":2017,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prognostics; Survival analysis; Estimator; Reliability (semiconductor); Set (abstract data type); Covariate; Statistics; Computer science; Data mining; Reliability engineering; Mathematics; Engineering","score_opus":0.05793070159335487,"score_gpt":0.3303299445134436,"score_spread":0.2723992429200887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589626016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24065489,0.000095279815,0.7558358,0.0016315939,0.00089880003,0.000460891,0.00016848042,0.0002009739,0.000053336094],"genre_scores_gemma":[0.9365681,0.00011175207,0.06276439,0.00012950368,0.00010387153,0.000099359524,0.00016857548,0.000033286502,0.00002115353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863136,0.00008952628,0.00044045658,0.0003730169,0.00018460244,0.00028101963],"domain_scores_gemma":[0.99820316,0.00084605935,0.00012500276,0.00034570612,0.00038342102,0.00009668217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011878484,0.00022801734,0.0003395926,0.00008939685,0.00019621546,0.00011349212,0.00022005691,0.00015802376,0.000027470416],"category_scores_gemma":[0.0030258268,0.00022591947,0.00007364399,0.00006460083,0.00016283499,0.00026121028,0.000041756488,0.00020228661,6.7655833e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002543859,0.0002652913,0.07807782,0.0079924,0.0003035221,0.0000062666268,0.00020327531,0.8922652,0.009338842,0.006258763,0.0006001199,0.004434122],"study_design_scores_gemma":[0.00089823,0.000037280486,0.09326354,0.00032990117,0.000033998323,0.000003521748,0.000009366783,0.9029189,0.0010080654,0.0006676223,0.0005585051,0.00027110172],"about_ca_topic_score_codex":0.000074631054,"about_ca_topic_score_gemma":0.000015348158,"teacher_disagreement_score":0.6959132,"about_ca_system_score_codex":0.00010778071,"about_ca_system_score_gemma":0.000043571377,"threshold_uncertainty_score":0.9212729},"labels":[],"label_agreement":null},{"id":"W2619653818","doi":"10.1002/qre.2157","title":"Phase I monitoring with nonparametric mixed‐effect models","year":2017,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Qazvin Islamic Azad University","keywords":"Nonparametric statistics; Smoothing; Computer science; Stability (learning theory); Kernel density estimation; Data mining; Kernel (algebra); Basis (linear algebra); Process (computing); Algorithm; Mathematics; Statistics; Machine learning","score_opus":0.12002963511936059,"score_gpt":0.4588307505219472,"score_spread":0.3388011154025866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619653818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5704515,0.000039182498,0.4272528,0.00032051766,0.0011238051,0.00009793894,0.000025378115,0.000053865966,0.00063500245],"genre_scores_gemma":[0.97598284,0.0000134782085,0.023530662,0.000005716939,0.00025668892,0.000022859891,0.0000022194747,0.00001295517,0.00017256883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973891,0.000068019304,0.00053220015,0.00057177193,0.0012052254,0.0002336968],"domain_scores_gemma":[0.9958796,0.0026093028,0.00024853324,0.0007269982,0.00036779352,0.00016780816],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0026874857,0.00018597976,0.0003129818,0.00015677376,0.000320963,0.00059500337,0.0008195617,0.00007860915,0.000016740509],"category_scores_gemma":[0.015544664,0.00013337696,0.000060780232,0.0001356149,0.00014577871,0.0010513836,0.00022075298,0.00029253366,0.000012981876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090364635,0.0007790435,0.18667708,0.00025473334,0.00016590278,0.000055891727,0.0005978755,0.4585084,0.0016131542,0.04564155,0.0000972709,0.30470544],"study_design_scores_gemma":[0.0042769583,0.00062601693,0.2424997,0.00019936844,0.00003338017,0.00002420928,0.00013813475,0.6734858,0.006077557,0.069502145,0.0023270317,0.0008096828],"about_ca_topic_score_codex":0.00005263933,"about_ca_topic_score_gemma":0.0000012492765,"teacher_disagreement_score":0.40553135,"about_ca_system_score_codex":0.00008866057,"about_ca_system_score_gemma":0.000021584492,"threshold_uncertainty_score":0.99274784},"labels":[],"label_agreement":null},{"id":"W2644985284","doi":"10.1002/qre.2180","title":"A new reliability analysis method for repairable systems with multifunction modes based on goal‐oriented methodology","year":2017,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Industry and Information Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Fault tree analysis; Reliability (semiconductor); Computer science; Reliability engineering; Operator (biology); Monte Carlo method; Process (computing); Bitwise operation; Complex system; Function (biology); Engineering; Mathematics; Artificial intelligence","score_opus":0.056029965069334005,"score_gpt":0.3752264359369813,"score_spread":0.3191964708676473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2644985284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01256132,0.000024854326,0.9826617,0.0034024937,0.0004693256,0.00045375363,0.000040101153,0.0002549872,0.00013142212],"genre_scores_gemma":[0.36708122,0.000008783243,0.6322636,0.000060910035,0.00010575913,0.00010876048,0.000033104596,0.000012182909,0.0003256692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965202,0.0005187909,0.00065437454,0.001187405,0.00074156304,0.00037766175],"domain_scores_gemma":[0.9929877,0.003811286,0.00030715577,0.0019526964,0.0006985328,0.00024261046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075810966,0.00028030225,0.0006447196,0.00037817188,0.00043476906,0.00048430022,0.0010095959,0.00020807306,0.000016640864],"category_scores_gemma":[0.0081011215,0.00022616176,0.0003678782,0.00040662318,0.000098482684,0.00055084744,0.00020237481,0.00034476523,0.0000025926117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032842078,0.00018894068,0.018073125,0.00021375109,0.00042381935,0.00000163053,0.00011222107,0.9514777,0.00014186697,0.024722204,0.000062734514,0.0042535802],"study_design_scores_gemma":[0.0008095186,0.0001887269,0.04491984,0.0000439867,0.000116782656,0.000002010249,0.000023378907,0.9501835,0.0002982539,0.0010336711,0.0021228231,0.0002574716],"about_ca_topic_score_codex":0.0029712263,"about_ca_topic_score_gemma":0.00003973414,"teacher_disagreement_score":0.3545199,"about_ca_system_score_codex":0.00024333941,"about_ca_system_score_gemma":0.00015799608,"threshold_uncertainty_score":0.96983844},"labels":[],"label_agreement":null},{"id":"W2783663176","doi":"10.1002/qre.2255","title":"A new reliability analysis method for repairable systems with closed‐loop feedback links","year":2018,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Nuclear Engineering Thermal-Hydraulics","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Industry and Information Technology of the People's Republic of China; National Natural Science Foundation of China; Shanghai Nuclear Engineering Research and Design Institute","keywords":"Fault tree analysis; Reliability (semiconductor); Reliability block diagram; Reliability engineering; Computer science; Monte Carlo method; Process (computing); Function (biology); Power (physics); Engineering; Mathematics","score_opus":0.01346783784574278,"score_gpt":0.2747608880247863,"score_spread":0.26129305017904353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783663176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11526221,0.00010116719,0.88055766,0.00042047474,0.0011305055,0.0004423125,0.00007640648,0.0010222927,0.0009869843],"genre_scores_gemma":[0.70034385,0.000032077147,0.29771563,0.000060608985,0.00080057216,0.000075737094,0.000066076886,0.00010590932,0.0007995536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979217,0.000058237736,0.0006674866,0.0005793183,0.0003632889,0.00040994468],"domain_scores_gemma":[0.9982088,0.00048428177,0.000081200415,0.00067774236,0.00031232045,0.00023561918],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015707372,0.00035259622,0.0005452183,0.00024045621,0.000078178105,0.00013043714,0.0003419174,0.00034799744,0.00006852474],"category_scores_gemma":[0.00042787867,0.00033123637,0.00020764415,0.0004976337,0.000063052044,0.0002418525,0.00006741571,0.00045992673,0.000014925023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009736501,0.00004094688,0.0010040749,0.00039171436,0.0007203213,0.0000010879634,0.00025401684,0.99186754,0.00087764923,0.0028121606,0.00079108437,0.0011420556],"study_design_scores_gemma":[0.00059858034,0.00011702427,0.019437356,0.000057447254,0.00019853072,0.000009628912,0.00003104156,0.94629216,0.0005030958,0.00021394234,0.032095876,0.00044534093],"about_ca_topic_score_codex":0.0002792291,"about_ca_topic_score_gemma":0.000023395904,"teacher_disagreement_score":0.58508164,"about_ca_system_score_codex":0.00025393188,"about_ca_system_score_gemma":0.000039244194,"threshold_uncertainty_score":0.999914},"labels":[],"label_agreement":null},{"id":"W2895820455","doi":"10.1002/qre.2402","title":"On the performance of coefficient of variation charts in the presence of measurement errors","year":2018,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"EWMA chart; Control chart; Statistics; Shewhart individuals control chart; Chart; Coefficient of variation; X-bar chart; Covariate; Variation (astronomy); Mathematics; Computer science; Process (computing)","score_opus":0.11110040690145281,"score_gpt":0.38580027225472574,"score_spread":0.27469986535327295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895820455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91881794,0.000009524838,0.080119826,0.00053323444,0.0002857517,0.000103710794,0.00001038957,0.0000041943176,0.0001154082],"genre_scores_gemma":[0.9991633,0.0000032851074,0.00078304316,0.000012521127,0.000024955001,0.000005924764,2.6994516e-7,0.0000021149817,0.000004536531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99781823,0.000112089656,0.0005573815,0.00014717878,0.0012987459,0.0000663664],"domain_scores_gemma":[0.9964604,0.002495966,0.00019674357,0.00023888555,0.0005950108,0.000013013997],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0063320315,0.00005248969,0.0001180814,0.000059854166,0.000023193357,0.000009056638,0.00036442216,0.000023189177,0.000014964989],"category_scores_gemma":[0.014019795,0.00002848796,0.00002427817,0.00019982105,0.00016090799,0.00007447057,0.000046109475,0.00008975194,6.933844e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037578886,0.00066246244,0.043353923,0.00032168976,0.00003520263,3.0855875e-7,0.015016436,0.57999367,0.015585961,0.33934632,0.00007379562,0.0052344715],"study_design_scores_gemma":[0.00018344443,0.00018615332,0.591512,0.00014998319,0.0000033832096,4.6203382e-7,0.00032636948,0.3827385,0.013640057,0.011053234,0.00013876006,0.00006762447],"about_ca_topic_score_codex":0.000030533523,"about_ca_topic_score_gemma":0.0000024764,"teacher_disagreement_score":0.5481581,"about_ca_system_score_codex":0.000029406354,"about_ca_system_score_gemma":0.000019046396,"threshold_uncertainty_score":0.9942855},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.08610558698603847,"score_gpt":0.4012396759512537,"score_spread":0.3151340889652152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897440829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34116888,0.000034225617,0.6566325,0.00011056721,0.0017588726,0.0001010932,0.00003326729,0.000034285444,0.00012635486],"genre_scores_gemma":[0.9069355,0.0000047964404,0.0921052,0.000023044344,0.0008561617,0.000008936067,0.0000021894823,0.00001366508,0.00005052041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968902,0.00018010376,0.0011264547,0.00047412745,0.0010431719,0.00028595363],"domain_scores_gemma":[0.99496806,0.0033063525,0.00033472397,0.00039503444,0.00089645816,0.00009937703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004330418,0.00019266603,0.00034871665,0.00013245807,0.00026440326,0.00022821009,0.0005988875,0.00007841228,0.00013349272],"category_scores_gemma":[0.0068382807,0.00014448239,0.00007940079,0.0002855666,0.00020418719,0.0006250218,0.00023985968,0.00028699927,0.000004705339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040066184,0.0002163266,0.084712975,0.00018087601,0.00024208629,0.000003814776,0.0026682352,0.66559607,0.1439356,0.093016885,0.000011714317,0.009014756],"study_design_scores_gemma":[0.00085538975,0.000053991535,0.0303545,0.00020857768,0.000028153241,0.000011156559,0.00021287512,0.9287976,0.009823216,0.02876406,0.0005941444,0.00029630488],"about_ca_topic_score_codex":0.00016906821,"about_ca_topic_score_gemma":0.0000018623264,"teacher_disagreement_score":0.56576663,"about_ca_system_score_codex":0.000107215375,"about_ca_system_score_gemma":0.000063380314,"threshold_uncertainty_score":0.8186555},"labels":[],"label_agreement":null},{"id":"W2903255379","doi":"10.1002/qre.2428","title":"Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes","year":2018,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Qinglan Project of Jiangsu Province of China; China Scholarship Council; Nanjing Institute of Technology; Nanjing University of Aeronautics and Astronautics; University of Toronto; National Natural Science Foundation of China","keywords":"Unobservable; Control chart; Bayesian probability; Residual; Hidden Markov model; Failure mode and effects analysis; Computer science; Fault (geology); Preventive maintenance; Condition-based maintenance; Bayesian inference; Process (computing); Markov process; Engineering; Reliability engineering; Artificial intelligence; Econometrics; Mathematics; Statistics; Algorithm","score_opus":0.00982033292679253,"score_gpt":0.2760010790639216,"score_spread":0.26618074613712905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903255379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21056572,0.00001793158,0.7843408,0.0028218736,0.0006179768,0.0004796798,0.00011150204,0.00034736938,0.00069714466],"genre_scores_gemma":[0.9125409,0.000029339992,0.08621355,0.00018360373,0.0006361724,0.00013365409,0.000031009455,0.000039240822,0.00019254247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984194,0.000026747179,0.00046777574,0.00042849363,0.0002484091,0.00040916208],"domain_scores_gemma":[0.99897224,0.00016513106,0.000039902683,0.00032225798,0.00032003666,0.00018044024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076689036,0.00025083244,0.00026363373,0.00018589203,0.0000904466,0.00010226685,0.00026750096,0.00012659344,0.00002982795],"category_scores_gemma":[0.0010158682,0.00024806435,0.00010333178,0.00020205596,0.000078651836,0.00020035631,0.00007654588,0.00018071156,0.0000107742235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010069725,0.000043334407,0.00029785812,0.00017479334,0.000052327774,6.555001e-7,0.00051609235,0.9739447,0.0035755779,0.019332016,0.00040073934,0.0015611631],"study_design_scores_gemma":[0.0008545112,0.000120029545,0.0025609708,0.00008981991,0.0000122284455,0.000011639307,0.00011324922,0.97835386,0.003945834,0.0015348074,0.011973756,0.0004293188],"about_ca_topic_score_codex":0.00015930859,"about_ca_topic_score_gemma":0.000074877986,"teacher_disagreement_score":0.70197517,"about_ca_system_score_codex":0.00029935935,"about_ca_system_score_gemma":0.000043095035,"threshold_uncertainty_score":0.99999714},"labels":[],"label_agreement":null},{"id":"W2906209034","doi":"10.1002/qre.2442","title":"Parameter estimation for load‐sharing system subject to Wiener degradation process using the expectation‐maximization algorithm","year":2018,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Estimator; Expectation–maximization algorithm; Maximization; Computer science; Degradation (telecommunications); Reliability (semiconductor); Component (thermodynamics); Function (biology); Estimation theory; Load sharing; Process (computing); Interdependence; Mathematical optimization; Maximum likelihood; Algorithm; Mathematics; Statistics; Distributed computing","score_opus":0.021603777674966854,"score_gpt":0.292014791287289,"score_spread":0.27041101361232217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906209034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23483145,0.000013178453,0.76310295,0.0002351596,0.00093194534,0.0005326979,0.00003375689,0.00025498905,0.00006388003],"genre_scores_gemma":[0.8406223,0.0000063619063,0.15873303,0.00005460735,0.00028005362,0.00017645204,0.00007403578,0.000033052336,0.000020101314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854046,0.000030343535,0.00051383406,0.00035995597,0.0003254218,0.0002299888],"domain_scores_gemma":[0.9987357,0.00021997422,0.00007951613,0.0002685006,0.00062356045,0.000072727926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009528855,0.00019665071,0.00018393615,0.000106880645,0.00018392864,0.0001470236,0.000207191,0.000113284645,0.000008850309],"category_scores_gemma":[0.0009193421,0.00016816438,0.00006371873,0.00025643737,0.000053894204,0.00045827212,0.000035889912,0.00011439016,0.000006772681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028174967,0.000017405166,0.0004283683,0.00023327257,0.000029013905,9.596926e-8,0.0009999856,0.99406266,0.0003482043,0.001500213,0.000037420647,0.0023152053],"study_design_scores_gemma":[0.0002698927,0.00003698892,0.001808746,0.00009453554,0.000021943448,0.0000063503,0.00027981785,0.9941707,0.0019928473,0.00088680355,0.00021563435,0.00021574332],"about_ca_topic_score_codex":0.000029841967,"about_ca_topic_score_gemma":0.000007387468,"teacher_disagreement_score":0.60579085,"about_ca_system_score_codex":0.0004769737,"about_ca_system_score_gemma":0.000030110332,"threshold_uncertainty_score":0.6857545},"labels":[],"label_agreement":null},{"id":"W2917647106","doi":"10.1002/qre.1040","title":"Discussion (3): Jones–Johnson Paper","year":2009,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.011034141712087997,"score_gpt":0.28277432417025505,"score_spread":0.27174018245816706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917647106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034212444,0.000033895652,0.98447037,0.010413891,0.00066152913,0.00010153312,0.0000050360904,0.00023992668,0.0006525941],"genre_scores_gemma":[0.5754537,0.00008304198,0.42308676,0.000711292,0.00016060144,0.000011119482,0.00001486983,0.000009102434,0.00046950835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988758,0.00003839891,0.0002808269,0.0003723603,0.00027903586,0.00015358778],"domain_scores_gemma":[0.9993433,0.000087346205,0.00006000908,0.0002944025,0.00012721443,0.0000877738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036946122,0.00012925544,0.00013094295,0.00007732773,0.000065475615,0.0000882897,0.00032786737,0.00006668803,0.000025587251],"category_scores_gemma":[0.00037891706,0.00009550356,0.00005189916,0.00014723152,0.00002264369,0.00077030336,0.000093195194,0.00015861729,0.000009286021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051662177,0.000662609,0.0015847798,0.00006291278,0.00004705768,0.000013018581,0.001569903,0.460995,0.0063232593,0.36830756,0.00039377756,0.15998846],"study_design_scores_gemma":[0.00056677504,0.000061020906,0.13912019,0.00003525893,0.0000026782802,0.000015568032,0.000017039103,0.83173,0.000642212,0.008512168,0.018965818,0.00033128803],"about_ca_topic_score_codex":0.000007900887,"about_ca_topic_score_gemma":4.1420873e-7,"teacher_disagreement_score":0.57203245,"about_ca_system_score_codex":0.00008941808,"about_ca_system_score_gemma":0.000016027727,"threshold_uncertainty_score":0.38945222},"labels":[],"label_agreement":null},{"id":"W2962746665","doi":"10.1002/qre.2520","title":"Modeling and detecting change in temporal networks via the degree corrected stochastic block model","year":2019,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Division of Mathematical Sciences","keywords":"Stochastic block model; Computer science; Parametric statistics; Cohesion (chemistry); Stochastic process; Representation (politics); Data mining; Block (permutation group theory); Network model; Dynamic network analysis; Artificial intelligence; Mathematics; Statistics","score_opus":0.037387686755493144,"score_gpt":0.2818581359577885,"score_spread":0.24447044920229538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962746665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58185285,0.000032565706,0.4176915,0.000116231466,0.000085713,0.0001442649,0.0000031989584,0.00003531944,0.000038350438],"genre_scores_gemma":[0.9981611,0.000002962565,0.0015699994,0.000027683296,0.00015867219,0.000038890852,0.000014766092,0.000011083449,0.000014838259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990953,0.000039575927,0.0003094095,0.00025636065,0.00013537807,0.00016397053],"domain_scores_gemma":[0.99949783,0.00017014045,0.000054688975,0.00017939576,0.00005997629,0.000037986643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061077357,0.00013297828,0.00018300457,0.000062584644,0.000049159327,0.000046109122,0.0001329012,0.000046847883,0.000018558863],"category_scores_gemma":[0.000026991942,0.00011092654,0.00005327964,0.0001069517,0.000020551064,0.00010973305,0.00012705487,0.00030485712,6.6779745e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010700084,0.000021659296,0.038042817,0.000009725113,0.00002123892,7.302525e-8,0.00022433422,0.95777845,0.000043315493,0.0012737446,0.0000011710964,0.002572796],"study_design_scores_gemma":[0.00016119318,0.000008375369,0.010612108,0.000041395924,0.000008555174,8.9637354e-7,0.000040073504,0.98752576,0.000005099191,0.0014623775,0.0000063200637,0.00012783962],"about_ca_topic_score_codex":0.0013325885,"about_ca_topic_score_gemma":0.000057378904,"teacher_disagreement_score":0.41630825,"about_ca_system_score_codex":0.00003809579,"about_ca_system_score_gemma":0.000008419568,"threshold_uncertainty_score":0.4523453},"labels":[],"label_agreement":null},{"id":"W2986495668","doi":"10.1002/qre.2589","title":"Integrated multiresponse parameter and tolerance design with model parameter uncertainty","year":2019,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Robustness (evolution); Mathematical optimization; Tolerance analysis; Computer science; Quality (philosophy); Reliability engineering; Mathematics; Engineering","score_opus":0.10957677726956226,"score_gpt":0.3960173430410653,"score_spread":0.286440565771503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986495668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6246834,0.00003466201,0.3744031,0.00029722147,0.000187372,0.00023426567,0.000020582098,0.000042953314,0.000096454954],"genre_scores_gemma":[0.67996246,0.000006650927,0.3192826,0.00015423963,0.000012974348,0.000023246586,0.0000034681943,0.000010895452,0.00054348196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726975,0.0004336903,0.0005947599,0.00069938967,0.0007828385,0.00021954993],"domain_scores_gemma":[0.9933667,0.005656544,0.00012270536,0.00044767355,0.00026930525,0.00013704028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004577665,0.00022890019,0.00036009555,0.00014684141,0.000048401183,0.00024118359,0.00034779552,0.000118333046,0.00011385731],"category_scores_gemma":[0.0048435796,0.00015624621,0.000065417764,0.0002064144,0.00014498309,0.00040038876,0.00011492213,0.0002646407,0.00002107291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013756139,0.00010495293,0.012672579,0.000022666218,0.00004006884,0.0000027502983,0.0007543154,0.9661066,0.0099679185,0.002200216,0.00006264742,0.0066896756],"study_design_scores_gemma":[0.0005988972,0.00011986072,0.022901414,0.000034584868,0.0000045102174,0.0000111505,0.00011917173,0.9708247,0.0015875897,0.0029881485,0.0005725518,0.00023741784],"about_ca_topic_score_codex":0.00007031471,"about_ca_topic_score_gemma":0.0000012313403,"teacher_disagreement_score":0.055279057,"about_ca_system_score_codex":0.00008780967,"about_ca_system_score_gemma":0.000050803865,"threshold_uncertainty_score":0.63715357},"labels":[],"label_agreement":null},{"id":"W2996726760","doi":"10.1002/qre.2595","title":"CUSUM control charts with variable sampling interval for monitoring the ratio of two normal variables","year":2019,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"CUSUM; EWMA chart; Control chart; X-bar chart; Statistics; Chart; Interval (graph theory); Control limits; Statistical process control; Shewhart individuals control chart; Computer science; Variable (mathematics); Sampling (signal processing); Mathematics; Process (computing)","score_opus":0.06337255087407491,"score_gpt":0.39185055182842016,"score_spread":0.32847800095434526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996726760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19758052,0.000028889839,0.8004416,0.00031892717,0.0011105486,0.00025727798,0.000075997305,0.000025957148,0.00016026378],"genre_scores_gemma":[0.93063587,0.00000247784,0.06887886,0.000021092033,0.00024361623,0.000038405313,0.0000038222333,0.000010798601,0.00016507243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795395,0.000063576255,0.00069022755,0.0003729415,0.0007152992,0.00020401609],"domain_scores_gemma":[0.99317765,0.005609799,0.00021647799,0.00031524771,0.00061896944,0.000061852545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003208189,0.00014017436,0.00029744022,0.0000694812,0.00008922865,0.00012548962,0.0004276686,0.000049409366,0.00005109587],"category_scores_gemma":[0.0044359053,0.00008939969,0.000056783676,0.00015448219,0.00007355709,0.00039230092,0.000084745196,0.00019153462,0.000003530236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003595058,0.00007525146,0.10449052,0.00017447476,0.00009011356,2.7697345e-7,0.0004986262,0.7524302,0.0072078407,0.13232403,0.000010715154,0.0023384558],"study_design_scores_gemma":[0.0044980766,0.00036580864,0.11619665,0.00043220873,0.00006539061,0.000014204059,0.00088011136,0.78124136,0.0064886953,0.08083235,0.008297955,0.0006872118],"about_ca_topic_score_codex":0.000046070894,"about_ca_topic_score_gemma":9.733265e-7,"teacher_disagreement_score":0.73305535,"about_ca_system_score_codex":0.000054294527,"about_ca_system_score_gemma":0.00004381281,"threshold_uncertainty_score":0.53105134},"labels":[],"label_agreement":null},{"id":"W3001537166","doi":"10.1002/qre.2623","title":"Confidence limits for compliance testing using mixed acceptance criteria","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Moratuwa; National Research Council Sri Lanka; Faculty of Graduate Studies and Research, University of Regina","keywords":"Confidence interval; Statistics; Parametric statistics; Limit (mathematics); Sample (material); Acceptance testing; Mathematics; Quality (philosophy); Econometrics; Computer science","score_opus":0.5045208900524006,"score_gpt":0.5088491805428861,"score_spread":0.0043282904904854425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001537166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107875094,0.000052672156,0.8889342,0.0017849954,0.000911147,0.00015836877,0.00010284427,0.000080237936,0.00010044766],"genre_scores_gemma":[0.7749391,0.0000021356057,0.22456741,0.00017731838,0.0002684795,0.000012053671,0.0000030717442,0.0000096884605,0.000020746722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977811,0.000055690394,0.0007304253,0.00058882637,0.0006292199,0.0002147677],"domain_scores_gemma":[0.99377006,0.0048864405,0.00018484339,0.00020685836,0.0007785498,0.00017324091],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0013443513,0.00014927897,0.00027807348,0.000042565007,0.00012312407,0.00020829783,0.00049390446,0.000060713777,0.000037102178],"category_scores_gemma":[0.057203796,0.00013850797,0.000054767996,0.0002549744,0.000087667475,0.00044166722,0.000114594906,0.00017616584,0.000007375348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066926103,0.00022413983,0.0493985,0.0015056256,0.00010511995,0.000016579774,0.0024606148,0.5628316,0.15460114,0.15523316,0.00079598516,0.07215829],"study_design_scores_gemma":[0.00029006758,0.000039817558,0.032518107,0.00007696398,0.000005363145,0.0000037841687,0.00012735784,0.93224394,0.001799886,0.030848937,0.0018204413,0.0002253087],"about_ca_topic_score_codex":0.000013068261,"about_ca_topic_score_gemma":6.717766e-7,"teacher_disagreement_score":0.667064,"about_ca_system_score_codex":0.00006207058,"about_ca_system_score_gemma":0.00003340597,"threshold_uncertainty_score":0.9507378},"labels":[],"label_agreement":null},{"id":"W3005517811","doi":"10.1002/qre.2636","title":"Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Multivariable calculus; Estimator; Wiener process; Markov process; Univariate; Computer science; Stochastic process; Degradation (telecommunications); Brownian motion; Mathematical optimization; Control theory (sociology); Mathematics; Engineering; Applied mathematics; Statistics; Multivariate statistics; Control engineering; Artificial intelligence; Machine learning","score_opus":0.01747506278217821,"score_gpt":0.2226593193288464,"score_spread":0.20518425654666816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005517811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15045802,0.000060186132,0.8471377,0.0006206297,0.0005392323,0.0005990343,0.00010148852,0.00037637586,0.00010733195],"genre_scores_gemma":[0.9887048,0.000057474645,0.010502056,0.0000587216,0.00023252593,0.00017597135,0.00019785846,0.00003760246,0.00003296917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986703,0.000021802725,0.00047195275,0.0003602343,0.0002631737,0.00021255633],"domain_scores_gemma":[0.9990064,0.00029043228,0.000078035126,0.00014854188,0.0003098135,0.00016676006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044299717,0.00020837132,0.0002511528,0.000064186606,0.00008972559,0.00010542249,0.00012631081,0.00013414034,0.000007710397],"category_scores_gemma":[0.0017329158,0.00018718526,0.000039772338,0.00018865167,0.00003505284,0.00041129524,0.000027963268,0.00018790984,0.0000014501272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001229477,0.00003182376,0.0013103972,0.0013547079,0.000045279467,2.2979981e-7,0.00052741397,0.99513716,0.00078584265,0.00046539638,0.0001055155,0.00011330196],"study_design_scores_gemma":[0.0007921427,0.00010058511,0.0055099227,0.00020365602,0.000025175947,0.000002925673,0.00019118562,0.9916444,0.00012628273,0.000035763085,0.0011480193,0.00021993721],"about_ca_topic_score_codex":0.000029395966,"about_ca_topic_score_gemma":0.0000022653085,"teacher_disagreement_score":0.8382468,"about_ca_system_score_codex":0.00011541798,"about_ca_system_score_gemma":0.000052136547,"threshold_uncertainty_score":0.7633193},"labels":[],"label_agreement":null},{"id":"W3027141429","doi":"10.1002/qre.2665","title":"Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Estimator; Robustness (evolution); Exponential function; M-estimator; Mathematics; Context (archaeology); Inference; Maximum likelihood; Statistics; Applied mathematics; Computer science; Artificial intelligence; Mathematical analysis","score_opus":0.4810620625288178,"score_gpt":0.4205550189352064,"score_spread":0.060507043593611365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027141429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03026037,0.000004442704,0.9639097,0.0038556894,0.00003714124,0.0002475818,0.001447667,0.000135222,0.00010221884],"genre_scores_gemma":[0.68274367,0.000001921237,0.31652206,0.00013219618,0.00007171992,0.00004262822,0.0004645604,0.000009488273,0.000011776264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998953,0.000018579192,0.00034344126,0.00033606347,0.00022617163,0.00012276343],"domain_scores_gemma":[0.99701804,0.0022596747,0.00008796162,0.00024641046,0.00027273473,0.00011516872],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00026219236,0.00011661245,0.00015415878,0.000018046412,0.00007958805,0.000064607135,0.00025216362,0.000051843977,0.00003598418],"category_scores_gemma":[0.008717465,0.000108547814,0.000019957823,0.00009030618,0.000048475787,0.00016731027,0.00011517886,0.00012114081,0.0000029295968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009341578,0.00031997176,0.001483438,0.0005713651,0.000078660094,2.692657e-7,0.00018464621,0.51297164,0.0010886304,0.48205397,0.0005868961,0.0005671394],"study_design_scores_gemma":[0.00041011733,0.00002082475,0.0055186013,0.000045436405,0.000026912938,6.739534e-7,0.000039065497,0.98594016,0.0001358045,0.0074677537,0.00024898618,0.00014565639],"about_ca_topic_score_codex":0.000019895355,"about_ca_topic_score_gemma":0.000005336573,"teacher_disagreement_score":0.6524833,"about_ca_system_score_codex":0.00002972138,"about_ca_system_score_gemma":0.0000507216,"threshold_uncertainty_score":0.99963254},"labels":[],"label_agreement":null},{"id":"W3037639229","doi":"10.1002/qre.2692","title":"New efficient exponentially weighted moving average variability charts based on auxiliary information","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"EWMA chart; Control chart; Statistics; Estimator; Regression; Regression analysis; Mathematics; Chart; Statistical process control; Computer science; Process (computing)","score_opus":0.0436512852378996,"score_gpt":0.33969960620094447,"score_spread":0.2960483209630449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037639229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06608931,0.000004518571,0.92664355,0.005093017,0.0009803774,0.00018081882,0.00007100362,0.0001448376,0.00079257734],"genre_scores_gemma":[0.96458715,0.0000014313848,0.034528926,0.00057632773,0.00023519785,0.000009366412,0.000023794219,0.000008435827,0.000029394325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663323,0.00015097784,0.00097633357,0.0005169158,0.0015050725,0.00021748073],"domain_scores_gemma":[0.99615467,0.002625011,0.0001970409,0.00034688183,0.00035025674,0.00032616896],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002559468,0.00019542368,0.00026921128,0.00012757738,0.00010634358,0.0002355196,0.0004415663,0.00009976389,0.00032259698],"category_scores_gemma":[0.019928038,0.00016875072,0.0000898349,0.00030173518,0.00004477485,0.00053210073,0.00014879268,0.0003367501,0.0000711104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020495034,0.00007187649,0.003051403,0.00007869012,0.000012174505,0.0000023781986,0.00066879566,0.9619266,0.00031829902,0.0157072,0.00020445854,0.017753169],"study_design_scores_gemma":[0.0004545952,0.000046825397,0.032217737,0.000026840473,0.0000040090017,6.2324153e-7,0.000032503292,0.95737445,0.00038266156,0.005055721,0.0042064614,0.00019753886],"about_ca_topic_score_codex":0.000026584521,"about_ca_topic_score_gemma":2.5460534e-7,"teacher_disagreement_score":0.8984978,"about_ca_system_score_codex":0.00013225694,"about_ca_system_score_gemma":0.00008975484,"threshold_uncertainty_score":0.9883275},"labels":[],"label_agreement":null},{"id":"W3049131949","doi":"10.1002/qre.2726","title":"Design of a variable sampling interval exponentially weighted moving average median control chart in presence of measurement errors","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"EWMA chart; Control chart; Statistics; Chart; Covariate; X-bar chart; Observational error; Mathematics; Interval (graph theory); Statistical process control; Sampling interval; Sampling (signal processing); Variable (mathematics); Computer science; Control theory (sociology); Algorithm; Control (management); Process (computing); Artificial intelligence; Filter (signal processing)","score_opus":0.159725675012907,"score_gpt":0.3737518051756652,"score_spread":0.2140261301627582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049131949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07965662,0.000042825384,0.9189832,0.00069853436,0.00035979727,0.0001763488,0.00003134185,0.000029213372,0.00002207239],"genre_scores_gemma":[0.91165996,0.000006041467,0.08823128,0.000026946282,0.000051316343,0.000011430344,0.000001426886,0.000008765536,0.0000028414531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967389,0.0001886887,0.0011601298,0.00038905966,0.0013586534,0.00016458314],"domain_scores_gemma":[0.9963926,0.0024585016,0.0002644438,0.00018558635,0.0005850365,0.000113800575],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004416166,0.00012458829,0.00038466766,0.00012065576,0.000020190935,0.000030388506,0.00042898906,0.00006336008,0.00004618793],"category_scores_gemma":[0.022976402,0.000111199224,0.00004833572,0.00026704525,0.00007586377,0.00027878443,0.000116175295,0.00021165043,0.0000010566613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034949373,0.000115794544,0.011509668,0.00024080323,0.000043245138,0.000002197702,0.002124987,0.91962004,0.058847554,0.0049629956,0.000006096225,0.0021771095],"study_design_scores_gemma":[0.00078167487,0.00007563095,0.023772936,0.00019756633,0.0000071443565,8.085605e-7,0.00020278036,0.9602763,0.003175659,0.011253372,0.00009916371,0.00015694929],"about_ca_topic_score_codex":0.00008556877,"about_ca_topic_score_gemma":0.0000028898796,"teacher_disagreement_score":0.83200336,"about_ca_system_score_codex":0.00007733759,"about_ca_system_score_gemma":0.0000657112,"threshold_uncertainty_score":0.9852535},"labels":[],"label_agreement":null},{"id":"W3087146381","doi":"10.1002/qre.2762","title":"Inverse Gaussian process model with frailty term in reliability analysis","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Inverse Gaussian distribution; Estimator; Covariate; Context (archaeology); Reliability (semiconductor); Gaussian; Computer science; Gaussian process; Process (computing); Algorithm; Statistics; Gamma process; Inverse; Applied mathematics; Data mining; Mathematics; Distribution (mathematics)","score_opus":0.01881183577315044,"score_gpt":0.2590735520717327,"score_spread":0.24026171629858228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087146381","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75718594,0.000018664607,0.23917061,0.0020634602,0.00011039803,0.00026427693,0.000053863627,0.00033481081,0.00079798844],"genre_scores_gemma":[0.98770106,0.00006687714,0.011850011,0.00016262886,0.00004980096,0.000049833227,0.00006970639,0.000025469632,0.000024621002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982332,0.00003779832,0.00058823766,0.0005223594,0.00033984234,0.00027858187],"domain_scores_gemma":[0.999214,0.00009461477,0.000058121284,0.00030109004,0.00013881747,0.00019333456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005283933,0.0002629667,0.0004053744,0.00016500914,0.00003689033,0.000060011047,0.00024085859,0.00016366667,0.000047952704],"category_scores_gemma":[0.00043679462,0.00023714761,0.00010605854,0.00068203703,0.00008698207,0.0004321473,0.000046398545,0.00041371412,0.0000037204377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000682853,0.000054712356,0.031334266,0.0003332716,0.00008697038,0.0000021102483,0.0010567121,0.9661636,0.00013423602,0.000603684,0.000022102287,0.00014005367],"study_design_scores_gemma":[0.00045809292,0.00002907068,0.061510384,0.000034056364,0.00004861422,0.0000010581782,0.000088763074,0.9368995,0.00016257633,0.00034309496,0.0001343176,0.00029049255],"about_ca_topic_score_codex":0.000044609875,"about_ca_topic_score_gemma":0.00003795739,"teacher_disagreement_score":0.23051512,"about_ca_system_score_codex":0.00017057046,"about_ca_system_score_gemma":0.000038545633,"threshold_uncertainty_score":0.96705985},"labels":[],"label_agreement":null},{"id":"W3101590272","doi":"10.1002/qre.2805","title":"Dynamic failure analysis of renewable energy systems in the remote offshore environments","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Downtime; Turbine; Offshore wind power; Renewable energy; Submarine pipeline; Reliability engineering; Engineering; Catastrophic failure; Marine engineering; Environmental science","score_opus":0.010901429195784507,"score_gpt":0.22620209942951322,"score_spread":0.2153006702337287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101590272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5029726,0.0014546763,0.48821273,0.003648361,0.0011603097,0.00045227134,0.00030397414,0.00023306988,0.0015619908],"genre_scores_gemma":[0.9991468,0.00014838505,0.0005054894,0.000072094546,0.000026636608,0.000011277413,0.00004514758,0.000011004541,0.00003315598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859196,0.00008144515,0.000582599,0.00025706558,0.00031497556,0.00017194281],"domain_scores_gemma":[0.99936146,0.00019928363,0.00006226737,0.0002876556,0.000026297605,0.00006306515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000695084,0.00015563039,0.00035795622,0.00012133526,0.000018419365,0.000028866056,0.0003197399,0.00010711286,0.00001328697],"category_scores_gemma":[0.00019880288,0.00012753718,0.00012083,0.0004229209,0.00004463178,0.00010536136,0.000044619228,0.00017153153,0.0000012373281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011253927,0.000026669364,0.0016953882,0.00022173405,0.0002059641,0.000001839267,0.000704717,0.99518996,0.00092640653,0.0007557622,0.00012557508,0.00013472237],"study_design_scores_gemma":[0.00017041978,0.000022336751,0.02797839,0.000042755968,0.000056356897,0.0000018169318,0.00023474058,0.955627,0.000076506134,0.000061134975,0.015588063,0.00014047374],"about_ca_topic_score_codex":0.0007251043,"about_ca_topic_score_gemma":0.00013608608,"teacher_disagreement_score":0.49617422,"about_ca_system_score_codex":0.00009581261,"about_ca_system_score_gemma":0.000008048728,"threshold_uncertainty_score":0.5200815},"labels":[],"label_agreement":null},{"id":"W3172720700","doi":"10.1002/qre.2867","title":"A multi‐state <i>k</i>‐out‐of‐<i>n</i>:F balanced system with a rebalancing mechanism","year":2021,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability (semiconductor); Component (thermodynamics); State (computer science); Markov chain; Markov process; Reliability engineering; Computer science; Process (computing); Product (mathematics); Mathematical optimization; Mechanism (biology); Engineering; Mathematics; Algorithm; Power (physics); Statistics; Physics","score_opus":0.008388500919382256,"score_gpt":0.21846437986288672,"score_spread":0.21007587894350446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172720700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29954398,0.000109766384,0.6978804,0.00017270824,0.0010517597,0.00017580044,0.000065831904,0.00039548546,0.0006043017],"genre_scores_gemma":[0.9483955,0.00014288246,0.051211864,0.000025152474,0.000046301237,0.000027294871,0.000034649398,0.000027374328,0.00008896974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986979,0.00004019832,0.0004879088,0.0003019419,0.00026199096,0.00021009552],"domain_scores_gemma":[0.99914545,0.00012329107,0.00006775947,0.0002882208,0.0002926319,0.00008264346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004905694,0.00018766228,0.0003070261,0.00005821658,0.000035297253,0.000039950086,0.00012100433,0.00009449666,0.000012381145],"category_scores_gemma":[0.00021500075,0.0001739501,0.00006943017,0.0001472646,0.00004157775,0.00018162344,0.00004581074,0.00020173607,0.000002796263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050834868,0.00008282578,0.001485799,0.0016365962,0.00012933223,0.000015388587,0.0008556506,0.9383449,0.046016883,0.01098973,0.000026565984,0.0003654663],"study_design_scores_gemma":[0.0010216289,0.000035653753,0.008136995,0.00047176456,0.000020642494,0.000036085545,0.0003027737,0.9617961,0.026965702,0.00021549384,0.0006251839,0.00037200152],"about_ca_topic_score_codex":0.000027140928,"about_ca_topic_score_gemma":0.000015946665,"teacher_disagreement_score":0.6488515,"about_ca_system_score_codex":0.00014035677,"about_ca_system_score_gemma":0.000036541205,"threshold_uncertainty_score":0.7093479},"labels":[],"label_agreement":null},{"id":"W3179274894","doi":"10.1002/qre.2944","title":"Monitoring dynamic networks: A simulation‐based strategy for comparing monitoring methods and a comparative study","year":2021,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; City University of Hong Kong; National Science Foundation","keywords":"Variety (cybernetics); Computer science; Context (archaeology); Dynamic network analysis; Data mining; Artificial intelligence; Computer network","score_opus":0.09820103649548916,"score_gpt":0.4492626000142635,"score_spread":0.3510615635187743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179274894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47265643,0.00009119113,0.5267978,0.000027726908,0.00015533535,0.00016773764,0.000005930987,0.00005125689,0.00004658426],"genre_scores_gemma":[0.9400111,0.0000033194174,0.059594665,0.0000018145489,0.00024866167,0.000080046644,0.00002465632,0.00001016332,0.000025586469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873716,0.00014156578,0.0004242283,0.0003802345,0.00014692187,0.00016990793],"domain_scores_gemma":[0.99814564,0.0012503148,0.0000994185,0.00019821765,0.00023903779,0.000067358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078331155,0.00017451007,0.00034930385,0.00006476361,0.00012361036,0.00014230396,0.000105063635,0.00003731348,0.000013151759],"category_scores_gemma":[0.00007546522,0.00018723465,0.000092169204,0.00013765151,0.00002656786,0.00012656667,0.00009488108,0.00019224825,1.4599368e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017390483,0.00013928433,0.21258228,0.000022163136,0.0001342343,2.689381e-7,0.00019312536,0.78164506,0.00019998838,0.0009463712,6.1925147e-7,0.004119232],"study_design_scores_gemma":[0.00040904112,0.000027877953,0.14609055,0.000051180297,0.000040087194,1.6278888e-7,0.0006105451,0.85138637,0.00047744854,0.00066852017,0.00007784718,0.00016036254],"about_ca_topic_score_codex":0.000070175134,"about_ca_topic_score_gemma":0.0000031712054,"teacher_disagreement_score":0.46735466,"about_ca_system_score_codex":0.00006476551,"about_ca_system_score_gemma":0.000022345503,"threshold_uncertainty_score":0.7635208},"labels":[],"label_agreement":null},{"id":"W3214156277","doi":"10.1002/qre.3014","title":"EM‐based likelihood inference for one‐shot device test data under log‐normal lifetimes and the optimal design of a CSALT plan","year":2021,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weibull distribution; Log-normal distribution; Censoring (clinical trials); Exponential distribution; Inference; Statistics; Hazard; Mathematics; Computer science; Artificial intelligence","score_opus":0.2177567831726209,"score_gpt":0.4084876332903099,"score_spread":0.190730850117689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214156277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01397353,0.00003359429,0.9809208,0.003292578,0.0000514834,0.00025338284,0.0013950232,0.00003631261,0.000043303156],"genre_scores_gemma":[0.8228564,0.00001550116,0.17644621,0.00014395933,0.000034748104,0.000057002184,0.00042003745,0.000006884328,0.000019218041],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989905,0.00006925829,0.00040783218,0.00023109984,0.0001938407,0.000107476386],"domain_scores_gemma":[0.9909508,0.008287287,0.00009947495,0.00031615427,0.0002872256,0.000059060654],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0010040306,0.00009680467,0.00018431473,0.000021937529,0.000076877346,0.000053249943,0.00019762601,0.000059214188,0.00006160907],"category_scores_gemma":[0.009810156,0.00007942626,0.000029962659,0.00007473618,0.00011634047,0.00008316738,0.000107404405,0.00010990234,0.0000010481723],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011754584,0.00045023477,0.0004448908,0.00044738688,0.00008341105,3.1664172e-7,0.00026037233,0.016459865,0.00044148238,0.9804779,0.00033691502,0.00047966078],"study_design_scores_gemma":[0.0012693581,0.00002767172,0.015056783,0.000071782815,0.00006184176,0.0000037104746,0.00014832958,0.94545054,0.0006937181,0.036733456,0.00034115132,0.00014162782],"about_ca_topic_score_codex":0.000013406716,"about_ca_topic_score_gemma":0.0000041884546,"teacher_disagreement_score":0.9437445,"about_ca_system_score_codex":0.000020852596,"about_ca_system_score_gemma":0.000089446985,"threshold_uncertainty_score":0.9985306},"labels":[],"label_agreement":null},{"id":"W4200211005","doi":"10.1002/qre.3031","title":"Optimal designs of constant‐stress accelerated life‐tests for one‐shot devices with model misspecification analysis","year":2021,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia, Innovación y Universidades","keywords":"Weibull distribution; Reliability (semiconductor); Log-normal distribution; Accelerated life testing; Constant (computer programming); Statistics; Optimal design; Design of experiments; Computer science; Reliability engineering; Mathematics; Engineering","score_opus":0.31567544457774,"score_gpt":0.4298398059172657,"score_spread":0.11416436133952573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200211005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14973533,0.000013299139,0.84773725,0.0010756179,0.000019582601,0.00015934503,0.0010318803,0.00004661783,0.00018108932],"genre_scores_gemma":[0.795133,0.000007323418,0.20418812,0.00003202945,0.000014437814,0.000053559874,0.0005212161,0.0000065561576,0.00004377291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890995,0.000032689113,0.00047439153,0.00025222485,0.00022780917,0.000102933715],"domain_scores_gemma":[0.9977996,0.00096284633,0.00014776319,0.00020284479,0.0008003723,0.000086568994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003132628,0.00010525175,0.00024419132,0.000064097825,0.00005566957,0.00004250299,0.00009859932,0.00006410839,0.000103305945],"category_scores_gemma":[0.001662163,0.00010041314,0.00007094074,0.0002800309,0.00006489478,0.00008051082,0.000020211088,0.000075933574,6.726508e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054158812,0.00037971014,0.0014444677,0.00026786787,0.00034624254,2.2877703e-7,0.00012492915,0.19853184,0.0019074503,0.79679966,0.00006734163,0.00007612442],"study_design_scores_gemma":[0.00045296317,0.00001907776,0.032894954,0.000045385572,0.00026289426,0.0000012518782,0.00010992497,0.9534665,0.004314949,0.008180219,0.00008334658,0.0001685563],"about_ca_topic_score_codex":0.000006890532,"about_ca_topic_score_gemma":0.000008329418,"teacher_disagreement_score":0.7886194,"about_ca_system_score_codex":0.000043365642,"about_ca_system_score_gemma":0.000084248146,"threshold_uncertainty_score":0.40947288},"labels":[],"label_agreement":null},{"id":"W4200374034","doi":"10.1002/qre.3029","title":"Normalization of gearbox vibration signal for tooth crack diagnosis under variable speed conditions","year":2021,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Normalization (sociology); Vibration; Acoustics; Structural engineering; Computer science; Control theory (sociology); Engineering; Artificial intelligence; Physics","score_opus":0.014141962156531459,"score_gpt":0.2573368742862064,"score_spread":0.24319491212967492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200374034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51416767,0.000060763326,0.48434263,0.00031469375,0.00038409646,0.00010664748,0.00020617497,0.00009172156,0.00032560778],"genre_scores_gemma":[0.99215186,0.00005628226,0.0071715354,0.000034928198,0.0000754909,0.000020284064,0.00038653205,0.000014360268,0.00008874253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921817,0.000017784796,0.00033720047,0.00016096923,0.00015728084,0.00010860633],"domain_scores_gemma":[0.9993294,0.00021666664,0.000039609815,0.00012637017,0.00024615208,0.000041826275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028080784,0.00009655452,0.00016224761,0.00007356946,0.000038927385,0.000044949233,0.00006715904,0.000081978345,0.00015226919],"category_scores_gemma":[0.00017761748,0.00010770555,0.00008001406,0.00015060068,0.000019691388,0.00011835626,0.000024264824,0.00008101156,0.0000012885969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004471397,0.00007301582,0.0026491585,0.00020021736,0.000100130834,2.2739722e-7,0.000060417242,0.9552587,0.016097711,0.025420237,0.00004632832,0.000089356094],"study_design_scores_gemma":[0.00025818593,0.000016635735,0.026332704,0.00003630487,0.00004146896,0.0000019026933,0.00004112729,0.9616355,0.008456947,0.0022475861,0.00078973395,0.00014194477],"about_ca_topic_score_codex":0.00004005498,"about_ca_topic_score_gemma":0.000009508064,"teacher_disagreement_score":0.4779842,"about_ca_system_score_codex":0.00005919254,"about_ca_system_score_gemma":0.00002365102,"threshold_uncertainty_score":0.43921047},"labels":[],"label_agreement":null},{"id":"W4210867473","doi":"10.1002/qre.3078","title":"Analyzing count data with measurement error","year":2022,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Statistics; Observational error; Inference; Log-normal distribution; Count data; Statistical inference; Regression analysis; Mathematics; Population; Regression; Econometrics; Computer science; Artificial intelligence","score_opus":0.22382170575779223,"score_gpt":0.43416051263235916,"score_spread":0.21033880687456694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210867473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08929152,0.00013690202,0.9059949,0.002606159,0.0010341527,0.00013972525,0.0003252613,0.00008394911,0.0003874608],"genre_scores_gemma":[0.98246133,0.000003228239,0.017257072,0.00004538897,0.000087809836,0.000020594756,0.000024500874,0.0000084875655,0.00009156122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962733,0.00010901578,0.0005180069,0.00059494487,0.0023434337,0.00016129117],"domain_scores_gemma":[0.9978327,0.0008946903,0.0001315443,0.00066180533,0.0003898347,0.00008942962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065599037,0.00010982166,0.00018245935,0.000100549136,0.0002059971,0.000116580246,0.00093356526,0.000019490652,0.00019639406],"category_scores_gemma":[0.0069362493,0.000086862274,0.000023133485,0.0002551037,0.000053097945,0.00034727645,0.00069841807,0.00028672416,0.0000047275967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029360503,0.00029322042,0.07935799,0.00008158203,0.00012290773,0.000019635094,0.00061190815,0.86790293,0.0006509567,0.029485675,0.0009092652,0.020270338],"study_design_scores_gemma":[0.0008958147,0.00014753551,0.18509305,0.000040227158,0.000027577627,0.000030402458,0.001099999,0.6603325,0.000096629534,0.019551028,0.13209838,0.00058687566],"about_ca_topic_score_codex":0.000052759373,"about_ca_topic_score_gemma":0.000007559994,"teacher_disagreement_score":0.8931698,"about_ca_system_score_codex":0.0002423052,"about_ca_system_score_gemma":0.00005947928,"threshold_uncertainty_score":0.83038396},"labels":[],"label_agreement":null},{"id":"W4220879730","doi":"10.1002/qre.3094","title":"Monitoring multivariate coefficient of variation for high‐dimensional processes","year":2022,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Statistics; Lasso (programming language); Multivariate statistics; Control chart; Mathematics; Covariance matrix; Covariance; Statistic; Coefficient of variation; Computer science; Process (computing)","score_opus":0.0799178848084482,"score_gpt":0.4074605306157077,"score_spread":0.3275426458072595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220879730","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36766466,0.000049609716,0.6296748,0.00038364902,0.001742135,0.00018164163,0.00024942568,0.000036188685,0.00001790889],"genre_scores_gemma":[0.95736504,0.0000022769084,0.042296283,0.000011399417,0.00012843306,0.00008564098,0.000012855594,0.000008924963,0.000089175825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974541,0.0000771004,0.0007383523,0.00041175648,0.0011712378,0.00014744724],"domain_scores_gemma":[0.9947471,0.0039635063,0.00025451483,0.00019336895,0.0007811716,0.00006034634],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0027415196,0.0001074568,0.00021899998,0.00013458653,0.00018274593,0.00004306411,0.0003269262,0.000034294648,0.000059664984],"category_scores_gemma":[0.016198928,0.00009825946,0.00004546494,0.00028542758,0.000041639578,0.0001833018,0.00022663099,0.00015594502,0.000001015566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002052873,0.00018889073,0.0047586546,0.00012966826,0.00002416437,5.495555e-7,0.00058528053,0.96340084,0.0045423713,0.022029804,0.000017288965,0.0041172197],"study_design_scores_gemma":[0.0016368552,0.00026427268,0.34223416,0.00007489702,0.000024998128,0.0000068782797,0.00044913727,0.55165225,0.009970368,0.09054376,0.002653434,0.00048899406],"about_ca_topic_score_codex":0.000054786167,"about_ca_topic_score_gemma":3.4355833e-7,"teacher_disagreement_score":0.58970034,"about_ca_system_score_codex":0.0001230473,"about_ca_system_score_gemma":0.00006465521,"threshold_uncertainty_score":0.992088},"labels":[],"label_agreement":null},{"id":"W4312058202","doi":"10.1002/qre.3253","title":"A study on inspection schemes in optimal design of control charts for deteriorating processes","year":2022,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Fredericton; University of New Brunswick; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Weibull distribution; Constant (computer programming); Control chart; Sampling (signal processing); Hazard; Interval (graph theory); Mathematical optimization; Computer science; Scheme (mathematics); Function (biology); Process (computing); Reliability engineering; Mathematics; Statistics; Engineering","score_opus":0.15620549749120138,"score_gpt":0.44231721247117095,"score_spread":0.28611171497996957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312058202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47624698,0.000011734347,0.52302283,0.000118466574,0.00022619052,0.0003057511,0.000039554325,0.00002194623,0.0000065308955],"genre_scores_gemma":[0.98448217,8.271743e-7,0.01520244,0.0000135419905,0.000044066488,0.00023678344,0.0000017836051,0.0000065856702,0.0000118035005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807405,0.00013221543,0.0006390768,0.00034485947,0.00069528056,0.000114533825],"domain_scores_gemma":[0.9958328,0.0035554657,0.00016727831,0.00013364902,0.00027935984,0.00003145724],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0033209985,0.00009069754,0.00021749573,0.00016539042,0.000104384264,0.00004181639,0.00022355576,0.000021339141,0.000011675835],"category_scores_gemma":[0.015404468,0.00008210499,0.000026616928,0.00024919753,0.00003042418,0.0001754364,0.00007313557,0.00015995587,3.6938263e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037895917,0.00035999485,0.02300065,0.000051725587,0.000012677419,0.0000016131069,0.001355634,0.97064006,0.0007009997,0.0016710705,0.0000043699815,0.0018222582],"study_design_scores_gemma":[0.0038170577,0.0018581249,0.13911869,0.000072414696,0.000014245432,0.000005821914,0.007948599,0.83170754,0.0018299612,0.012533879,0.00064792135,0.00044576183],"about_ca_topic_score_codex":0.000010023977,"about_ca_topic_score_gemma":0.0000011108218,"teacher_disagreement_score":0.5082352,"about_ca_system_score_codex":0.000103824634,"about_ca_system_score_gemma":0.000042209067,"threshold_uncertainty_score":0.9928892},"labels":[],"label_agreement":null},{"id":"W4312059830","doi":"10.1002/qre.3247","title":"Progressive system safety and reliability analysis: A sustainable game theory approach","year":2022,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Reliability (semiconductor); Game theory; Computer science; Cooperative game theory; Reliability theory; Decision theory; Management science; Operations research; Sequential game; Risk analysis (engineering); Reliability engineering; Engineering; Microeconomics; Economics; Business","score_opus":0.004370383571100554,"score_gpt":0.22732279841524616,"score_spread":0.2229524148441456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312059830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6532694,0.00078848837,0.34037265,0.0002840154,0.0004382153,0.00065656775,0.0002040566,0.0007427784,0.0032437977],"genre_scores_gemma":[0.99720925,0.000031262032,0.002224423,0.00002363139,0.000079400575,0.00014579728,0.000102453276,0.000021728865,0.00016204489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974452,0.0003094873,0.000701276,0.00060239236,0.0005416784,0.00039991253],"domain_scores_gemma":[0.99874336,0.00035469854,0.000093309536,0.00049226626,0.00016946775,0.0001469051],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00329947,0.00028444317,0.00053306785,0.00033061116,0.0002993013,0.00010769087,0.0003175683,0.00011483974,0.00012766736],"category_scores_gemma":[0.00038697632,0.0002746323,0.00021953484,0.0007386637,0.00014542637,0.00027280595,0.00027980545,0.00060439203,9.692559e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000922855,0.0000605614,0.0044730715,0.00068151474,0.0004249264,0.000007278666,0.0006932461,0.9495306,0.000023850373,0.04365004,0.000013514408,0.00034911756],"study_design_scores_gemma":[0.00039425204,0.00004364562,0.057483066,0.000013471046,0.0003110997,0.00004283224,0.003887532,0.9325679,0.000043882952,0.0015749473,0.0031926103,0.00044476835],"about_ca_topic_score_codex":0.00009134054,"about_ca_topic_score_gemma":7.308481e-7,"teacher_disagreement_score":0.34393984,"about_ca_system_score_codex":0.0006056443,"about_ca_system_score_gemma":0.000034866513,"threshold_uncertainty_score":0.9999706},"labels":[],"label_agreement":null},{"id":"W4317435533","doi":"10.1002/qre.3271","title":"Fault tree analysis improvements: A bibliometric analysis and literature review","year":2023,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":172,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; Postdoctoral Research Foundation of China; Sun Yat-sen University","keywords":"Scopus; Fault tree analysis; Frontier; Data science; China; Computer science; Operations research; Regional science; Management science; Political science; Engineering; Geography","score_opus":0.04722463717243495,"score_gpt":0.39626013260226,"score_spread":0.34903549542982504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317435533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8916415,0.014371159,0.07199904,0.01964614,0.00051272806,0.00034609143,0.0005084088,0.0002855395,0.0006893798],"genre_scores_gemma":[0.9219923,0.073168635,0.0019413679,0.0004480842,0.000093405026,0.000033351418,0.0003128505,0.000010686781,0.0019993323],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99604815,0.00020470087,0.0011460697,0.000798789,0.0015621659,0.00024014608],"domain_scores_gemma":[0.99667144,0.0015929762,0.00024455087,0.0006580357,0.0006508221,0.00018217094],"candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.007348766,0.0002069348,0.00076531194,0.04374742,0.00011367616,0.00041499286,0.00050511514,0.00012034876,0.00021248998],"category_scores_gemma":[0.007929188,0.00015350581,0.00064620964,0.22165973,0.00006211537,0.0004214446,0.00024189318,0.00023275861,0.000029380755],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071937946,0.00023195846,0.7174301,0.00075338944,0.012040707,0.000039711253,0.00094817946,0.056931976,0.00028682328,0.002912053,0.0031504552,0.20520271],"study_design_scores_gemma":[0.0001680753,0.000016154576,0.830302,0.00006584404,0.0011720377,0.0000016076307,0.000057203768,0.16092327,0.000011882552,0.0016139372,0.005474019,0.00019397703],"about_ca_topic_score_codex":0.00013729958,"about_ca_topic_score_gemma":0.00007300022,"teacher_disagreement_score":0.20500875,"about_ca_system_score_codex":0.000046370962,"about_ca_system_score_gemma":0.000015645657,"threshold_uncertainty_score":0.9670909},"labels":[],"label_agreement":null},{"id":"W4321239522","doi":"10.1002/qre.3287","title":"Robust inference for nondestructive one‐shot device testing under step‐stress model with exponential lifetimes","year":2023,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Educación, Cultura y Deporte; Ministerio de Ciencia, Innovación y Universidades","keywords":"Accelerated life testing; Censoring (clinical trials); Quantile; Estimator; Exponential distribution; Statistics; Robustness (evolution); Inference; Reliability (semiconductor); Statistical inference; Computer science; Statistical hypothesis testing; Stress (linguistics); Exponential function; Reliability engineering; Mathematics; Weibull distribution; Engineering; Power (physics); Artificial intelligence","score_opus":0.28140066861417795,"score_gpt":0.4079010989045551,"score_spread":0.12650043029037716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321239522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12919208,0.0000017207987,0.8685132,0.00094558514,0.00006173294,0.00025144545,0.0006270825,0.00021328518,0.00019381421],"genre_scores_gemma":[0.7501505,0.0000022727074,0.24928094,0.00003425183,0.000050705436,0.00015482206,0.000209892,0.0000133450785,0.00010328626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894375,0.000021749662,0.00033710673,0.00027636735,0.00025738575,0.0001636579],"domain_scores_gemma":[0.9971686,0.0021345261,0.00008941132,0.00015620426,0.00036978384,0.00008148246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037238366,0.00012745548,0.00016077672,0.00005756288,0.00010940709,0.00005919765,0.00012369911,0.00006732301,0.000026927266],"category_scores_gemma":[0.0028094216,0.00011910516,0.000034105076,0.00017963079,0.000057754154,0.000114886265,0.000050126982,0.00012380726,0.0000055815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027694545,0.000097733086,0.0004707895,0.00019909613,0.00003899662,2.536059e-7,0.00010263788,0.28662613,0.00035430604,0.71173316,0.00010206804,0.0002471182],"study_design_scores_gemma":[0.00036093825,0.00002057932,0.024063835,0.00007913384,0.000023858964,0.0000013873206,0.000094815645,0.9223883,0.00022353866,0.052535806,0.00004400467,0.00016380873],"about_ca_topic_score_codex":0.0000221498,"about_ca_topic_score_gemma":0.0000052166306,"teacher_disagreement_score":0.6591974,"about_ca_system_score_codex":0.00006449296,"about_ca_system_score_gemma":0.000049852384,"threshold_uncertainty_score":0.48569673},"labels":[],"label_agreement":null},{"id":"W4361285183","doi":"10.1002/qre.3331","title":"Is designed data collection still relevant in the big data era? – A discussion","year":2023,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Robustness (evolution); Data collection; Variance (accounting); Data science; Big data; Research design; Management science; Statistics; Data mining; Mathematics; Engineering","score_opus":0.09944997944038245,"score_gpt":0.34847735036604677,"score_spread":0.24902737092566432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361285183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049496586,0.00001869362,0.9800563,0.013337783,0.001058018,0.0002377499,0.00011823587,0.00018071638,0.000042885997],"genre_scores_gemma":[0.59811157,0.0004639523,0.3982577,0.0007680531,0.00042056452,0.00007967354,0.0011552466,0.00003348099,0.00070975866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825567,0.0001466779,0.00034580097,0.0006652902,0.000417064,0.00016947057],"domain_scores_gemma":[0.9978338,0.00053025165,0.00007331997,0.0014299911,0.000090582376,0.000042079126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022641183,0.0001215098,0.00012579892,0.00015884753,0.00008490474,0.00013436523,0.0017971268,0.00006015566,0.0000054225447],"category_scores_gemma":[0.0017776982,0.00007900716,0.000019908626,0.0007401859,0.000036423662,0.00079351827,0.0011167962,0.00022414968,0.0000092965465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002121163,0.0012913634,0.014684266,0.0004437358,0.0002119701,0.00007526186,0.023311239,0.686403,0.0024115846,0.026670743,0.014434664,0.2298501],"study_design_scores_gemma":[0.00030770566,0.000014242793,0.049378846,0.00002548184,0.000002387215,0.0000066051784,0.00007552216,0.9425597,0.000033918168,0.00087423617,0.0066002933,0.00012103098],"about_ca_topic_score_codex":0.00009188912,"about_ca_topic_score_gemma":0.000018140383,"teacher_disagreement_score":0.5931619,"about_ca_system_score_codex":0.00008234441,"about_ca_system_score_gemma":0.000058890273,"threshold_uncertainty_score":0.33395386},"labels":[],"label_agreement":null},{"id":"W4388100841","doi":"10.1002/qre.3466","title":"Special issue on systems reliability and safety","year":2023,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Computer science; Engineering; Physics","score_opus":0.022380856244474855,"score_gpt":0.30645469886519855,"score_spread":0.2840738426207237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388100841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8050038,0.00019125611,0.12513831,0.04241669,0.011810338,0.0013573464,0.00020905463,0.0027569765,0.011116212],"genre_scores_gemma":[0.9819524,0.0010555737,0.0060201287,0.00031760917,0.007392415,0.00010513369,0.000081131555,0.000039102513,0.0030364513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973325,0.00020062365,0.0005771187,0.0007898795,0.0007594345,0.0003404058],"domain_scores_gemma":[0.99739736,0.0014584054,0.000070145274,0.00066508615,0.0002103431,0.00019863776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033991968,0.00020590218,0.00032305377,0.00024419307,0.00016791538,0.00029053597,0.0005796741,0.00015003182,0.000050450522],"category_scores_gemma":[0.0025541466,0.00018504774,0.00010482695,0.0005407111,0.00011855927,0.00035630044,0.0004368196,0.0003778246,0.00010281232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037920213,0.00081899704,0.052595187,0.0018608065,0.00034469806,0.0000570851,0.0029208225,0.39608055,0.00033049565,0.4249105,0.019837584,0.09986409],"study_design_scores_gemma":[0.00053621747,0.00010005366,0.16591243,0.000086524175,0.000007120225,0.000010142132,0.00007709843,0.5557914,0.00006477014,0.0025919294,0.27443138,0.00039093505],"about_ca_topic_score_codex":0.00014829599,"about_ca_topic_score_gemma":0.0000021147714,"teacher_disagreement_score":0.42231855,"about_ca_system_score_codex":0.00014990098,"about_ca_system_score_gemma":0.000043166052,"threshold_uncertainty_score":0.7546028},"labels":[],"label_agreement":null},{"id":"W4391820589","doi":"10.1002/qre.3504","title":"A risk‐based fuzzy arithmetic model to determine safety integrity levels considering individual and societal risks","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fuzzy logic; Reliability engineering; Fuzzy number; Risk analysis (engineering); Apportionment; Computer science; Fuzzy set; Interval (graph theory); Risk assessment; Engineering; Mathematics; Artificial intelligence","score_opus":0.21061093846863138,"score_gpt":0.4212670108658183,"score_spread":0.21065607239718692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391820589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6269575,0.0001781279,0.36562872,0.005850947,0.0004116961,0.00013284922,0.00054453954,0.000109304856,0.0001863772],"genre_scores_gemma":[0.96305454,0.0000810044,0.03638202,0.00019118615,0.00009689073,0.000014462426,0.000014298504,0.000013527051,0.0001520849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662614,0.00020138411,0.0009723894,0.0008143336,0.0011284513,0.00025729966],"domain_scores_gemma":[0.9958712,0.003153947,0.000101876445,0.00038228006,0.0002521219,0.00023859946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007222397,0.00024225947,0.00041874012,0.00035386282,0.00017472662,0.0005608982,0.0004022718,0.0001702625,0.00008219693],"category_scores_gemma":[0.0070984294,0.00019487042,0.00020032449,0.0004306217,0.0001436869,0.0003449415,0.00028732212,0.0006386282,0.000021363965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001267457,0.00013537965,0.027107079,0.00015036792,0.00032578423,0.00001784724,0.0035269656,0.8510134,0.0006331649,0.012557519,0.0004118129,0.10399391],"study_design_scores_gemma":[0.00022636892,0.000032902914,0.10706871,0.000053457017,0.00005051782,0.000009291695,0.00013404794,0.8643483,0.000102554994,0.025893064,0.0018371268,0.0002436846],"about_ca_topic_score_codex":0.00022552133,"about_ca_topic_score_gemma":0.000051633095,"teacher_disagreement_score":0.33609706,"about_ca_system_score_codex":0.00010508004,"about_ca_system_score_gemma":0.00010951175,"threshold_uncertainty_score":0.8497996},"labels":[],"label_agreement":null},{"id":"W4392196375","doi":"10.1002/qre.3513","title":"Multi‐objective Bayesian modeling and optimization of 3D printing process via experimental data‐driven method","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Process (computing); Computer science; 3D printing; Quality (philosophy); Bayesian probability; Product (mathematics); Bayesian optimization; Mathematical optimization; Industrial engineering; Data mining; Engineering; Machine learning; Artificial intelligence; Mathematics; Mechanical engineering","score_opus":0.023787753268127248,"score_gpt":0.31962891566286533,"score_spread":0.29584116239473807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392196375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05732395,0.00037146523,0.94146514,0.0000419707,0.00032858978,0.0001303931,0.000032840937,0.00023785201,0.00006780362],"genre_scores_gemma":[0.73800033,0.00006093089,0.26180163,0.0000034241784,0.000049630802,0.000011467278,0.000047177622,0.000019883233,0.0000055276682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900347,0.000021043525,0.00035566025,0.00033521268,0.00016872131,0.00011587255],"domain_scores_gemma":[0.99960226,0.000088699606,0.000030458026,0.00016216726,0.00006969721,0.000046744553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045681512,0.00014756146,0.00016706577,0.00011113123,0.000035624384,0.00007427596,0.00014236456,0.00008525762,0.000024229417],"category_scores_gemma":[0.0001051741,0.00014767938,0.000022568067,0.00008476742,0.000020714113,0.00043924895,0.00009353615,0.00016423706,2.7642508e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062889285,0.000022461882,0.00012197705,0.0007830909,0.000047702437,4.843937e-7,0.0008955136,0.99590695,0.0004700002,0.00011492,9.1066033e-7,0.001629713],"study_design_scores_gemma":[0.0001489892,0.000009084259,0.00021494956,0.00013358361,0.000013542094,0.000005632422,0.000092693874,0.99693936,0.002220168,0.00004047258,0.00002402591,0.00015751823],"about_ca_topic_score_codex":0.000038118345,"about_ca_topic_score_gemma":0.0000012364876,"teacher_disagreement_score":0.68067634,"about_ca_system_score_codex":0.00004195436,"about_ca_system_score_gemma":0.000013090773,"threshold_uncertainty_score":0.60221905},"labels":[],"label_agreement":null},{"id":"W4399052648","doi":"10.1002/qre.3580","title":"Advances and novel applications in systems reliability and safety engineering (selected papers of the International Conference of SRSE 2022)","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Christian ministry; China; Beijing; Reliability (semiconductor); Engineering; Engineering management; Political science; Law","score_opus":0.00881505897396057,"score_gpt":0.2330666372378419,"score_spread":0.22425157826388134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399052648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84592134,0.003812159,0.14361979,0.0010877284,0.0023764172,0.001090153,0.0004134525,0.00029989344,0.0013790663],"genre_scores_gemma":[0.9958752,0.0023212316,0.0016424187,0.000003991514,0.000037956037,0.000053640826,0.000023878594,0.000015252648,0.000026428126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864995,0.00002391916,0.0006318411,0.0003086995,0.00024606934,0.00013950781],"domain_scores_gemma":[0.99911433,0.00037407252,0.000059930124,0.00021511645,0.00018693994,0.0000496198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070365035,0.00017037043,0.00025629852,0.00012279038,0.000025442705,0.000050168663,0.00019370069,0.000116247895,0.000011356924],"category_scores_gemma":[0.0005505681,0.0001419954,0.00004749791,0.00028631632,0.00012809539,0.0002696269,0.00009349616,0.00026814238,1.4906125e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030027522,0.000064833475,0.0095855035,0.0027337912,0.00005708006,2.6183258e-7,0.0005250312,0.9143667,0.016010683,0.055302244,0.0000074300565,0.0013163867],"study_design_scores_gemma":[0.00028737856,0.000014983499,0.061591756,0.00042663634,0.000014739794,0.000006753074,0.00016016116,0.9309902,0.00069819397,0.00032663916,0.0053018797,0.00018066292],"about_ca_topic_score_codex":0.00008126974,"about_ca_topic_score_gemma":0.000013176332,"teacher_disagreement_score":0.14995386,"about_ca_system_score_codex":0.0001157884,"about_ca_system_score_gemma":0.00003344351,"threshold_uncertainty_score":0.57904047},"labels":[],"label_agreement":null},{"id":"W4399246352","doi":"10.1002/qre.3595","title":"Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Maintainability; Reliability engineering; Reliability (semiconductor); Covariate; Computer science; Estimation; Imperfect; Engineering","score_opus":0.010028671866666696,"score_gpt":0.2558451672653867,"score_spread":0.24581649539872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399246352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60276616,0.0003746373,0.3938605,0.0006176416,0.0009663284,0.00044008167,0.000093641866,0.0007285712,0.00015244496],"genre_scores_gemma":[0.9812345,0.0002305674,0.018260175,0.000016026668,0.00006298017,0.00006409331,0.00003592965,0.000038357095,0.0000573975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978388,0.000097412274,0.00085482583,0.0005826296,0.0003160598,0.00031028697],"domain_scores_gemma":[0.99863553,0.00050397206,0.00006264678,0.00043373878,0.00022784354,0.00013629044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019646771,0.0003137286,0.00040871845,0.0002157456,0.00006112673,0.00011452545,0.0001746004,0.00023280931,0.000020624426],"category_scores_gemma":[0.000905582,0.00028665995,0.00013133128,0.00031185427,0.00021161405,0.00051626423,0.000093289294,0.0003850677,0.00000455639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034839984,0.000059606613,0.00073056505,0.004217156,0.00009033271,0.0000030574704,0.00040723282,0.9478554,0.0012105658,0.04342111,0.00007819355,0.0018919108],"study_design_scores_gemma":[0.0003503761,0.00004000137,0.020553092,0.00041944673,0.000037980346,0.00003300912,0.0002121885,0.9753736,0.00052451127,0.0014193662,0.0007387199,0.00029768076],"about_ca_topic_score_codex":0.00009204081,"about_ca_topic_score_gemma":0.000008226574,"teacher_disagreement_score":0.3784683,"about_ca_system_score_codex":0.00050184457,"about_ca_system_score_gemma":0.000046122528,"threshold_uncertainty_score":0.9999586},"labels":[],"label_agreement":null},{"id":"W4400341623","doi":"10.1002/qre.3604","title":"Statistical inference of a series reliability system using shock models with Weibull distribution","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Weibull distribution; Series (stratigraphy); Statistical inference; Reliability (semiconductor); Reliability engineering; Inference; Statistics; Econometrics; Computer science; Mathematics; Engineering; Artificial intelligence; Geology","score_opus":0.061833691394169485,"score_gpt":0.3667783526469124,"score_spread":0.3049446612527429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400341623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119204246,0.000017041355,0.877374,0.0004160649,0.00013339797,0.00018207432,0.0023088597,0.00019233637,0.00017201583],"genre_scores_gemma":[0.9314591,0.0000059186646,0.068174094,0.000004288797,0.000031882377,0.000032587875,0.00025045755,0.000010547102,0.000031113188],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985675,0.00005774326,0.0005729474,0.00031120365,0.0003498357,0.000140734],"domain_scores_gemma":[0.99819106,0.0011267667,0.00007501645,0.00022026656,0.0002976521,0.000089210145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006294854,0.00014775657,0.00023590791,0.000040278897,0.00005909486,0.00006625221,0.00010478471,0.000083513354,0.00006401503],"category_scores_gemma":[0.0012465576,0.00012272457,0.00004414989,0.0001624661,0.00015467574,0.00024027588,0.00004768002,0.00017862397,0.0000026990351],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034346096,0.0000723672,0.00020758164,0.0011541373,0.000028088998,0.0000013856502,0.00008014536,0.01632662,0.000105074076,0.98177475,0.000047085225,0.00016839491],"study_design_scores_gemma":[0.00015656288,0.00003796388,0.0059338775,0.00031114576,0.000045974066,0.000022334632,0.000095177005,0.9194979,0.00018685481,0.07312646,0.0004152685,0.00017046228],"about_ca_topic_score_codex":0.00007693838,"about_ca_topic_score_gemma":0.0000019643564,"teacher_disagreement_score":0.9086483,"about_ca_system_score_codex":0.0002215525,"about_ca_system_score_gemma":0.00007686255,"threshold_uncertainty_score":0.5004562},"labels":[],"label_agreement":null},{"id":"W4402195061","doi":"10.1002/qre.3654","title":"Reliability test for degradation data based on ranked set sampling","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Evaluation and Optimization Models","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Xi’an Jiaotong-Liverpool University; Government of Jiangsu Province; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Reliability engineering; Reliability (semiconductor); Sampling (signal processing); Computer science; Statistics; Data set; Data mining; Degradation (telecommunications); Test (biology); Engineering; Mathematics; Biology","score_opus":0.1071854630515051,"score_gpt":0.36449784980074085,"score_spread":0.25731238674923573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402195061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01900405,0.00009472613,0.97519404,0.0017012628,0.0014935461,0.00039267805,0.00096152513,0.00078486674,0.0003733152],"genre_scores_gemma":[0.9404658,0.000056425397,0.056974076,0.00015828919,0.00024363445,0.000076996825,0.0018705733,0.000044388395,0.00010978959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854636,0.000031364794,0.00047963543,0.0004607191,0.00031889675,0.00016302895],"domain_scores_gemma":[0.99760896,0.0016419401,0.00002554494,0.0004987334,0.00014833841,0.00007650345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018038733,0.00018110914,0.00016791957,0.00013930802,0.000059711478,0.00016098103,0.00025702297,0.00011705271,0.000082717524],"category_scores_gemma":[0.0020255744,0.00017740342,0.00006541597,0.00014518524,0.00002788774,0.00035761853,0.000041741816,0.00020430016,0.000009802748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016568782,0.00003345652,0.000219653,0.00041261278,0.000023571709,1.9739524e-7,0.00006167603,0.9939399,0.00031513252,0.002929043,0.00070189935,0.0013463084],"study_design_scores_gemma":[0.00033029963,0.000023062883,0.002984267,0.00009687249,0.000017931343,8.682952e-7,0.000009834781,0.9783228,0.00015473658,0.0006157716,0.017242016,0.00020152153],"about_ca_topic_score_codex":0.000008186842,"about_ca_topic_score_gemma":0.000002246125,"teacher_disagreement_score":0.92146176,"about_ca_system_score_codex":0.00015556288,"about_ca_system_score_gemma":0.000042895364,"threshold_uncertainty_score":0.7234301},"labels":[],"label_agreement":null},{"id":"W4403821230","doi":"10.1002/qre.3673","title":"Joint modeling of degradation signals and time‐to‐event data for the prediction of remaining useful life","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Event (particle physics); Data mining; Hyperparameter; Joint (building); Artificial neural network; Bayesian inference; Bayesian probability; Reliability engineering; Artificial intelligence; Real-time computing; Engineering","score_opus":0.05123528661738318,"score_gpt":0.2774122798629017,"score_spread":0.2261769932455185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403821230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12012768,0.00042963141,0.877238,0.00087862636,0.00042757104,0.000344309,0.00039654702,0.00010494746,0.000052695337],"genre_scores_gemma":[0.98938406,0.00026582935,0.010039694,0.000014482918,0.00009013512,0.000024843761,0.00013415587,0.000017086712,0.000029746388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989088,0.000020719894,0.00054828846,0.00023725232,0.00018403039,0.00010088151],"domain_scores_gemma":[0.9990655,0.00043275213,0.000037553076,0.00028133724,0.00013445984,0.00004838421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015222287,0.00010540876,0.0001781088,0.00008760325,0.00003058022,0.00003716884,0.00014656929,0.000068484434,0.000016341524],"category_scores_gemma":[0.0011732706,0.000085697335,0.000045499877,0.00010380635,0.000031896925,0.0002492384,0.00008003186,0.00010607475,7.746807e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001769062,0.000013079706,0.000050663515,0.0006484536,0.0000693642,4.604031e-8,0.00028361543,0.9928968,0.0029715437,0.0016405552,0.00016627123,0.0012419489],"study_design_scores_gemma":[0.0001141558,0.000029691086,0.0010108456,0.00023507151,0.000030201969,0.0000014171945,0.00006630311,0.9966552,0.0004620623,0.00041933235,0.0009003072,0.00007541303],"about_ca_topic_score_codex":0.000024104605,"about_ca_topic_score_gemma":9.639119e-7,"teacher_disagreement_score":0.8692564,"about_ca_system_score_codex":0.00004314292,"about_ca_system_score_gemma":0.000024114463,"threshold_uncertainty_score":0.34946358},"labels":[],"label_agreement":null},{"id":"W4405766477","doi":"10.1002/qre.3714","title":"Joint Optimization of Condition‐Based Maintenance and Production Rate Using Reinforcement Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Kermanshah University of Technology","keywords":"Reinforcement learning; Markov decision process; Production (economics); Computer science; Time horizon; Production planning; Scheduling (production processes); Mathematical optimization; Q-learning; Production control; Hyperparameter; Preventive maintenance; Dynamic programming; Industrial engineering; Operations research; Markov process; Engineering; Algorithm; Reliability engineering; Machine learning; Mathematics","score_opus":0.017490954589299397,"score_gpt":0.2587951566424163,"score_spread":0.24130420205311692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405766477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12179229,0.00017823954,0.8758781,0.00043287058,0.0010798206,0.00021524982,0.000012028182,0.00028233186,0.00012907681],"genre_scores_gemma":[0.9694773,0.00039009756,0.029841583,0.000018513607,0.00009519923,0.000020364281,0.00006514084,0.000024386036,0.00006740978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887073,0.00004184783,0.0004834321,0.00027758582,0.0001776339,0.00014876133],"domain_scores_gemma":[0.99951327,0.00009610646,0.00005276077,0.0001258073,0.00016411343,0.000047943013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000905789,0.00015545546,0.00018814011,0.00015898769,0.000049132683,0.00006569369,0.00005283034,0.00009167391,0.000031674193],"category_scores_gemma":[0.00046193364,0.00015366805,0.00005331122,0.0001645207,0.00007877269,0.00034223113,0.000027469801,0.00021354116,8.400407e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011055421,0.000012603819,0.00010098733,0.00075229973,0.000032297572,7.0857584e-7,0.00014411673,0.9926322,0.0038711303,0.0017733872,0.00002275221,0.0006464697],"study_design_scores_gemma":[0.00017634904,0.00002679504,0.0009979272,0.0003281723,0.000017291954,0.0000069277785,0.000040813375,0.9940454,0.003296632,0.00022640802,0.0006724427,0.00016485072],"about_ca_topic_score_codex":0.00002871254,"about_ca_topic_score_gemma":5.515976e-7,"teacher_disagreement_score":0.84768504,"about_ca_system_score_codex":0.00015438588,"about_ca_system_score_gemma":0.000024490178,"threshold_uncertainty_score":0.6266401},"labels":[],"label_agreement":null},{"id":"W4407527988","doi":"10.1002/qre.3743","title":"Remaining Useful Life Prediction Through the Derivation of Acceleration Factors Based on Intermittent Inspection Data","year":2025,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Defense Acquisition Program Administration","keywords":"Reliability (semiconductor); Reliability engineering; Acceleration; Product (mathematics); Process (computing); Computer science; Field (mathematics); Product lifecycle; Data mining; Engineering; New product development; Mathematics; Power (physics)","score_opus":0.042295149530753216,"score_gpt":0.28632454827525816,"score_spread":0.24402939874450494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407527988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3839119,0.000034359768,0.6109892,0.0015883385,0.0016882155,0.00030173524,0.00009020473,0.00031487033,0.0010811486],"genre_scores_gemma":[0.9972837,0.00008792619,0.0020734386,0.00012154686,0.00006907443,0.00002150271,0.00031218104,0.000012601645,0.000018057817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986745,0.000059558573,0.0005785382,0.0003009524,0.00026120097,0.00012525266],"domain_scores_gemma":[0.9988304,0.00036337724,0.00008557138,0.00053759885,0.00015362896,0.00002945414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007167906,0.0001604917,0.00017840543,0.00011466388,0.00009626756,0.00006098134,0.00028635384,0.00012301956,0.000013705114],"category_scores_gemma":[0.0014826391,0.00012938704,0.00005293679,0.0002286853,0.000066845656,0.00046221874,0.00007213822,0.00024899005,6.937336e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039943,0.00005814329,0.00860182,0.00018961144,0.000044033834,6.431393e-8,0.00031922528,0.98392284,0.0005020781,0.0054825325,0.00036515426,0.00047458173],"study_design_scores_gemma":[0.0002512359,0.000025777397,0.1680927,0.00012777589,0.000015796752,2.6107193e-7,0.00014789334,0.8284318,0.0008521425,0.00033706104,0.0016259142,0.00009167934],"about_ca_topic_score_codex":0.00005873199,"about_ca_topic_score_gemma":0.0000063813454,"teacher_disagreement_score":0.6133718,"about_ca_system_score_codex":0.0001609368,"about_ca_system_score_gemma":0.000036194884,"threshold_uncertainty_score":0.527625},"labels":[],"label_agreement":null},{"id":"W4414035143","doi":"10.1002/qre.70066","title":"Dynamic Risk‐Adjusted Monitoring of Time Between Events: Applications of NHPP in Pipeline Accident Surveillance","year":2025,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pipeline (software); Accident (philosophy); Computer science; Reliability engineering; Forensic engineering; Statistics; Engineering; Mathematics","score_opus":0.02696868728546262,"score_gpt":0.3754776483612467,"score_spread":0.34850896107578405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414035143","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8547331,0.00014759428,0.14370528,0.000874648,0.00014883462,0.00013100072,0.00010127089,0.000016115746,0.00014212604],"genre_scores_gemma":[0.9966076,0.00028519263,0.0026839115,0.0000029610974,0.000024031444,0.00001306034,0.000018440891,0.000003360166,0.00036142053],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975966,0.00017477853,0.0012318457,0.00031277537,0.0005797107,0.00010429092],"domain_scores_gemma":[0.99673116,0.002139078,0.00030552098,0.00039394468,0.00039375146,0.000036556667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042332215,0.000098726756,0.00039406514,0.0003858826,0.000030463234,0.000015823467,0.00045973857,0.0000832723,0.00003851975],"category_scores_gemma":[0.0034766628,0.000086792905,0.00012491757,0.0007682191,0.00004981325,0.0001166722,0.00012486987,0.00016819128,0.000004950666],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018819319,0.00006165671,0.9322013,0.00002603117,0.00003589661,7.056096e-8,0.000084571024,0.05533623,0.00016846151,0.00030448323,0.00000849184,0.011753976],"study_design_scores_gemma":[0.00022860123,0.000006476909,0.9375383,0.000043745928,0.000011290154,1.1352833e-7,0.000087319466,0.054300625,0.00023678606,0.0071510347,0.00032789717,0.00006777309],"about_ca_topic_score_codex":0.00043866262,"about_ca_topic_score_gemma":0.000041260573,"teacher_disagreement_score":0.14187449,"about_ca_system_score_codex":0.00007894774,"about_ca_system_score_gemma":0.00003408556,"threshold_uncertainty_score":0.4162141},"labels":[],"label_agreement":null},{"id":"W4415824339","doi":"10.1002/qre.70113","title":"Robust Inference for Intermittently‐Monitored Step‐Stress Tests Under Weibull Lifetime Distributions","year":2025,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia, Innovación y Universidades","keywords":"Weibull distribution; Estimator; Robustness (evolution); Reliability (semiconductor); Censoring (clinical trials); Accelerated life testing; Statistical hypothesis testing; Statistical inference; Divergence (linguistics)","score_opus":0.06651659859037024,"score_gpt":0.3925111021160582,"score_spread":0.325994503525688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415824339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0220316,0.000012634262,0.9683663,0.0065954616,0.000350291,0.00034110155,0.0018625426,0.00015746745,0.00028256563],"genre_scores_gemma":[0.9508753,0.00000872091,0.04762148,0.000132028,0.000071732175,0.00024254841,0.00055212533,0.000009329051,0.0004867556],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988226,0.000034598204,0.0005066948,0.00029341487,0.00017147846,0.00017117687],"domain_scores_gemma":[0.99710846,0.002126574,0.00008582933,0.00024249726,0.00035041987,0.0000862273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003708687,0.00014893162,0.00019407486,0.00006595174,0.00013151849,0.000083261446,0.00019505645,0.00010416407,0.000060394752],"category_scores_gemma":[0.0047575654,0.00014600801,0.000080868886,0.00014281659,0.00009426638,0.000089706125,0.000080044745,0.00016017993,0.000005987172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016135693,0.00021185703,0.0014683047,0.00018774238,0.000045324563,1.26289e-7,0.00001973821,0.0024892916,0.0001020279,0.9930053,0.0019493208,0.0005048566],"study_design_scores_gemma":[0.0013000615,0.000040262657,0.32547352,0.00033915433,0.00009719627,0.000002866906,0.00012014209,0.40662378,0.0006759273,0.2528706,0.01198009,0.00047641786],"about_ca_topic_score_codex":0.000027887656,"about_ca_topic_score_gemma":0.000004092636,"teacher_disagreement_score":0.9288437,"about_ca_system_score_codex":0.00014535226,"about_ca_system_score_gemma":0.00004963572,"threshold_uncertainty_score":0.5954034},"labels":[],"label_agreement":null}]}