{"meta":{"query_hash":"5ad00a6abd58","filters":{"venue":"Quality Engineering"},"cohort_total":51,"direct_labels_cover":0,"predictions_cover":51,"exported":51,"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/5ad00a6abd58","api":"https://metacan.xera.ac/api/v1/cohort?venue=Quality+Engineering"},"results":[{"id":"W1550575192","doi":"","title":"Variance estimation with hot deck imputation simulation study of three methods","year":2006,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Statistics Canada","funders":"","keywords":"Statistics; Jackknife resampling; Estimator; Imputation (statistics); Econometrics; Population; Mathematics; Missing data; Point estimation; Demography","score_opus":0.09189468185055545,"score_gpt":0.447997944898127,"score_spread":0.35610326304757156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550575192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14035887,0.0012891942,0.85079455,0.00072172075,0.00011856463,0.0008175883,0.00031781936,0.00038542683,0.0051963152],"genre_scores_gemma":[0.60466534,0.0005222818,0.3901989,0.0003601413,0.00006334861,0.0018772627,0.0004921675,0.00020808206,0.0016125398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8603869,0.12791227,0.0018047745,0.0030533525,0.0057717543,0.0010708866],"domain_scores_gemma":[0.45376688,0.50336283,0.0070088636,0.024132317,0.010576563,0.0011525176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09491668,0.00084625074,0.0023641787,0.002121617,0.0010754019,0.003123825,0.0030982606,0.0023124646,0.0030678136],"category_scores_gemma":[0.30844602,0.0012168597,0.0028532206,0.002558357,0.0023003719,0.0043967217,0.0031366602,0.0029354235,0.00037737718],"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":[0.0024580532,0.0005323911,0.024697753,0.0005667963,0.0014911419,0.00019139476,0.0012907269,0.6905968,0.0004100785,0.18583092,0.0024576406,0.08947623],"study_design_scores_gemma":[0.0003432963,0.0005205315,0.0038420218,0.00018501986,0.00020687676,0.00015270729,0.00022362184,0.9415529,0.0006925222,0.05044302,0.0017355087,0.00010195604],"about_ca_topic_score_codex":0.0046262434,"about_ca_topic_score_gemma":0.0033428115,"teacher_disagreement_score":0.09491668,"about_ca_system_score_codex":0.0024423152,"about_ca_system_score_gemma":0.0026765384,"threshold_uncertainty_score":0.5019734},"labels":[],"label_agreement":null},{"id":"W1997719892","doi":"10.1081/qen-200056484","title":"Graphical Representation of Run Length Distributions","year":2005,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Chart; Control chart; Computer science; Representation (politics); Distribution (mathematics); Statistics; Algorithm; Data mining; Mathematics","score_opus":0.1446130501748025,"score_gpt":0.4720399405972798,"score_spread":0.3274268904224773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997719892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058666337,0.00008802401,0.98697376,0.00016141131,0.000037856345,0.000043394193,0.00062980107,0.00395305,0.0022460306],"genre_scores_gemma":[0.48352313,0.0003838784,0.50594676,0.00033673368,0.00018137447,0.0005981563,0.0026462474,0.002003524,0.0043802178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99513185,0.0020015363,0.000268411,0.0008332457,0.0014635295,0.0003014452],"domain_scores_gemma":[0.9527261,0.031945705,0.004529334,0.0037868496,0.0064253523,0.00058663235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044758124,0.0011388069,0.0006983531,0.0061902557,0.0005353821,0.0032006816,0.0015661706,0.0010258924,0.012571766],"category_scores_gemma":[0.03872801,0.0004295517,0.0008862815,0.003086769,0.001399553,0.002431336,0.0011713046,0.0020398952,0.0024208976],"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":[0.0006178404,0.00013538779,0.00872828,0.0004848164,0.00013861798,0.0006067787,0.0007164374,0.4210671,0.011280415,0.32356104,0.019401731,0.21326165],"study_design_scores_gemma":[0.0000664762,0.00008396057,0.0022412676,0.000125083,0.000029073723,0.0002074494,0.00006537285,0.8203833,0.004705548,0.16159385,0.01041098,0.00008759583],"about_ca_topic_score_codex":0.002864661,"about_ca_topic_score_gemma":0.001978064,"teacher_disagreement_score":0.012571766,"about_ca_system_score_codex":0.0013093032,"about_ca_system_score_gemma":0.0010419025,"threshold_uncertainty_score":0.04205668},"labels":[],"label_agreement":null},{"id":"W2004918350","doi":"10.1081/qen-120020778","title":"Applying Real-Time Statistical Process Control to Manufacturing Processes Exhibiting Between and Within Part Size Variability in the Wood Products Industry","year":2003,"lang":"en","type":"article","venue":"Quality Engineering","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 British Columbia","funders":"","keywords":"Statistical process control; Control chart; Process (computing); Process variation; Variation (astronomy); Engineering; Variance (accounting); Process control; Sampling (signal processing); Industrial engineering; Control (management); Statistical analysis; Computer science; Manufacturing engineering; Statistics; Mathematics; Artificial intelligence; Telecommunications; Accounting","score_opus":0.07107120375221034,"score_gpt":0.38598022052936076,"score_spread":0.31490901677715044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004918350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008037283,0.00009000296,0.991433,0.000021697995,0.000007997836,0.00002011028,0.000008975733,0.00028071002,0.00010020917],"genre_scores_gemma":[0.4827446,0.00037557626,0.5159633,0.00006142602,0.00006155409,0.00018441801,0.00009034682,0.00008159399,0.00043708662],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968501,0.0011035654,0.00015391028,0.000496729,0.0013142134,0.000081502236],"domain_scores_gemma":[0.9925143,0.004974794,0.0010053093,0.0006082841,0.00083041505,0.000066756744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033530022,0.00056624174,0.0005979816,0.0009989624,0.00030219308,0.00093091367,0.0009318179,0.00042361088,0.00037448952],"category_scores_gemma":[0.010267706,0.00024137016,0.00051369984,0.00095617684,0.00081365963,0.00073736993,0.0004069981,0.00061362865,0.000114425864],"study_design_candidate":"bench_or_experimental","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":[0.00021944505,0.00022565952,0.005222387,0.00026389264,0.00013386189,0.00013433506,0.00050639396,0.43085268,0.045113467,0.01841803,0.0005286182,0.49838126],"study_design_scores_gemma":[0.0000211285,0.0002816322,0.0017804992,0.000013001916,0.000030609935,0.00008958335,0.000029549681,0.9667888,0.023559814,0.0058071283,0.0015537315,0.000044562214],"about_ca_topic_score_codex":0.0029881387,"about_ca_topic_score_gemma":0.0025406168,"teacher_disagreement_score":0.0033530022,"about_ca_system_score_codex":0.00061445875,"about_ca_system_score_gemma":0.0011233573,"threshold_uncertainty_score":0.01773256},"labels":[],"label_agreement":null},{"id":"W2010002412","doi":"10.1081/qen-120020772","title":"Minimizing Cost of Multiple Response Systems by Probabilistic Robust Design","year":2003,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SNC-Lavalin (Canada); University of Waterloo","funders":"University of Waterloo","keywords":"Rework; Reliability engineering; Mathematical optimization; Probabilistic logic; Reliability (semiconductor); Product (mathematics); Quality (philosophy); Total cost; Probabilistic design; Manufacturing cost; Function (biology); Computer science; Engineering; Mathematics; Engineering design process; Power (physics)","score_opus":0.3095892073698175,"score_gpt":0.4293579533605675,"score_spread":0.11976874599074999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010002412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009016502,0.00018413861,0.9895705,0.00011591955,0.000008931735,0.00006291003,0.000015062224,0.00008092011,0.0009450126],"genre_scores_gemma":[0.67214054,0.0005807406,0.3236266,0.000098887314,0.00004506658,0.0010119235,0.00009941741,0.000094986426,0.0023017428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949138,0.0029016885,0.00015228066,0.0005042542,0.0013018077,0.00022613323],"domain_scores_gemma":[0.993153,0.005151672,0.00084646285,0.00028720486,0.00049450126,0.00006711451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069690337,0.0018361142,0.0014200817,0.0010157726,0.00040376763,0.0013434241,0.0012574741,0.0013232726,0.0017235129],"category_scores_gemma":[0.012304454,0.0008762877,0.0009947352,0.00081057305,0.0013573768,0.001658745,0.0012014718,0.0011805035,0.00027411184],"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":[0.00006316779,0.000025901914,0.00015595475,0.000074792944,0.000029599118,0.000014812245,0.000023208144,0.9661471,0.000734996,0.02053987,0.000089415866,0.012101112],"study_design_scores_gemma":[0.000025948639,0.000118809396,0.00009971206,0.000011174514,0.000018557765,0.0000108018985,0.000008002631,0.9859205,0.000893477,0.012407155,0.00047309644,0.000012637989],"about_ca_topic_score_codex":0.0017301671,"about_ca_topic_score_gemma":0.0011341976,"teacher_disagreement_score":0.0069690337,"about_ca_system_score_codex":0.0017557106,"about_ca_system_score_gemma":0.0019025565,"threshold_uncertainty_score":0.036856234},"labels":[],"label_agreement":null},{"id":"W2013992075","doi":"10.1080/08982110108918688","title":"AN INTEGRATED ECONOMIC DESIGN MODEL FOR QUALITY CONTROL, REPLACEMENT, AND MAINTENANCE","year":2001,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Quality (philosophy); Reliability engineering; Control (management); Manufacturing engineering; Business; Operations management; Engineering; Computer science","score_opus":0.030416631782877613,"score_gpt":0.27546791641363366,"score_spread":0.24505128463075604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013992075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025977273,0.00040640897,0.9470951,0.0008304029,0.0001057934,0.00042961098,0.00043349483,0.00027377697,0.024448022],"genre_scores_gemma":[0.83263296,0.0008960545,0.11987631,0.00024231517,0.00011826357,0.0016186382,0.00042314958,0.00015289638,0.04403943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774915,0.0007968624,0.000059847323,0.00039410495,0.0005495784,0.00045043472],"domain_scores_gemma":[0.99716526,0.0018867196,0.00029424898,0.0001035835,0.00041494932,0.00013524054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004509065,0.0019137594,0.0022302512,0.0013564457,0.00080498407,0.0028506957,0.0036267515,0.002845847,0.013816595],"category_scores_gemma":[0.008003634,0.0018757228,0.0013685967,0.0012611425,0.0016808488,0.002529346,0.0015451206,0.002125146,0.0011624132],"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":[0.000050499515,0.00004290291,0.00014073752,0.00004106576,0.000019029303,0.000026675378,0.000023040922,0.95338374,0.00037005846,0.04028381,0.0005470974,0.0050713513],"study_design_scores_gemma":[0.00003667959,0.000037932452,0.00013528786,0.0000070425413,0.000020799704,0.000008810963,0.000009280211,0.9897968,0.000119741984,0.009144665,0.0006730459,0.000009958823],"about_ca_topic_score_codex":0.013437418,"about_ca_topic_score_gemma":0.011764752,"teacher_disagreement_score":0.013816595,"about_ca_system_score_codex":0.0053590597,"about_ca_system_score_gemma":0.0040424294,"threshold_uncertainty_score":0.046221137},"labels":[],"label_agreement":null},{"id":"W2014994995","doi":"10.1080/08982110903579359","title":"George Box: A Source of Inspiration for Quality and Productivity","year":2010,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Complex Systems and Decision Making","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":"George (robot); Productivity; Quality (philosophy); Engineering; Marketing; Operations research; Industrial engineering; Advertising; Computer science; Economics; Business; Artificial intelligence; Epistemology; Economic growth; Philosophy","score_opus":0.20599379087737127,"score_gpt":0.4364426810811829,"score_spread":0.23044889020381162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014994995","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007547469,0.061431356,0.00940693,0.86288536,0.027083429,0.000018417883,0.00010455837,0.00014517823,0.038170084],"genre_scores_gemma":[0.09831667,0.063622765,0.020585177,0.4797021,0.038232498,0.00020705712,0.00016095847,0.0013258753,0.2978469],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950578,0.0020346397,0.00018181768,0.0005670784,0.0017898214,0.00036876713],"domain_scores_gemma":[0.98514915,0.0091440035,0.000678027,0.0006834149,0.002690839,0.001654575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063708588,0.0008098273,0.0009099741,0.0014490203,0.002871295,0.0075487196,0.0009012337,0.0066939513,0.013404651],"category_scores_gemma":[0.02688244,0.0004747135,0.00042771644,0.0016027679,0.008045596,0.011040679,0.003741039,0.014750982,0.0051350803],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025961,0.000010176349,0.00013594447,0.00009388362,0.0000076283213,0.00004766142,0.0010941958,0.000115451185,0.00009909913,0.18818155,0.793048,0.017140444],"study_design_scores_gemma":[0.000009497667,0.000009915345,0.00012311758,0.00026918526,0.0000034768013,0.000086026106,0.0004651483,0.0001444269,0.0001454714,0.073475994,0.925246,0.000021725597],"about_ca_topic_score_codex":0.0042942124,"about_ca_topic_score_gemma":0.0055850265,"teacher_disagreement_score":0.013404651,"about_ca_system_score_codex":0.00384552,"about_ca_system_score_gemma":0.0036006472,"threshold_uncertainty_score":0.04484296},"labels":[],"label_agreement":null},{"id":"W2020556972","doi":"10.1081/qen-100106893","title":"Interrelating Quality and Reliability in Engineering Systems","year":2002,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Systems Engineering Methodologies and Applications","field":"Engineering","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":"University of Waterloo","funders":"","keywords":"Engineering research; Network topology; Reliability (semiconductor); Engineering; Quality (philosophy); Graph; Engineering management; Management; Computer science; Operations research; Telecommunications; Theoretical computer science; Economics; Operating system","score_opus":0.10231001574765416,"score_gpt":0.30951207678968823,"score_spread":0.20720206104203406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020556972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05692992,0.0010272169,0.89939785,0.0018087183,0.00016460089,0.00008341018,0.00025714966,0.0020239356,0.038307276],"genre_scores_gemma":[0.819097,0.0010149092,0.15726137,0.0001745293,0.00015457779,0.00017131984,0.00046772388,0.00050279946,0.021155834],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834526,0.00057078776,0.000060181705,0.00019799854,0.0007318457,0.00009391257],"domain_scores_gemma":[0.99047416,0.005075726,0.00082187884,0.0015939998,0.0017571127,0.0002771534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019056641,0.00044480697,0.0003580072,0.001772169,0.00040327237,0.0012995257,0.00072095,0.0006996632,0.020532735],"category_scores_gemma":[0.012679011,0.0003741607,0.00027828477,0.0011915257,0.0008833005,0.0036297094,0.0011127414,0.00092462345,0.00298269],"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":[0.0004069989,0.00019331645,0.005336541,0.000614759,0.000067874826,0.00026717386,0.00078757614,0.109646805,0.025276043,0.27203763,0.027022962,0.55834234],"study_design_scores_gemma":[0.00008071351,0.000261065,0.008813551,0.000081399245,0.000045247947,0.00044921399,0.00042057177,0.428056,0.022520123,0.5003618,0.038827725,0.000082557715],"about_ca_topic_score_codex":0.0013148937,"about_ca_topic_score_gemma":0.0016135051,"teacher_disagreement_score":0.020532735,"about_ca_system_score_codex":0.0008484138,"about_ca_system_score_gemma":0.00031042667,"threshold_uncertainty_score":0.06868881},"labels":[],"label_agreement":null},{"id":"W203707744","doi":"","title":"A new face on two-phase sampling with calibration estimators","year":2010,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Rogers Communications (Canada)","funders":"","keywords":"Estimator; Categorical variable; Sampling (signal processing); Calibration; Mathematics; Phase (matter); Statistics; Population; Sample (material); Sample size determination; Sampling design; Range (aeronautics); Context (archaeology); Computer science; Engineering","score_opus":0.11759523154337344,"score_gpt":0.42095476869326554,"score_spread":0.3033595371498921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W203707744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009905671,0.0010931799,0.9903094,0.0038560363,0.00028379558,0.000100442616,0.000057151065,0.00007065969,0.0032387332],"genre_scores_gemma":[0.06634887,0.0021762906,0.91804576,0.0044599,0.0024792978,0.001228363,0.0001109188,0.0003064451,0.0048441594],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8916405,0.088417545,0.0023224351,0.006305912,0.010428956,0.000884664],"domain_scores_gemma":[0.77640265,0.18971136,0.0055500004,0.020417934,0.006975681,0.0009424496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09068739,0.0018025408,0.0029623653,0.0025003979,0.0020703697,0.006359049,0.004947589,0.0060856645,0.00927556],"category_scores_gemma":[0.22171134,0.0019934278,0.003163721,0.0031745788,0.013708413,0.012946539,0.007482723,0.01598572,0.0015125163],"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":[0.000046907146,0.00004036259,0.0009328489,0.00016383306,0.00006416793,0.00007143202,0.0005583717,0.0063063246,0.00011732921,0.9501929,0.0029498828,0.038555592],"study_design_scores_gemma":[0.00007550695,0.00013385898,0.0003917793,0.0002297678,0.00003197873,0.0001337124,0.00013070977,0.035059698,0.0003775913,0.9262996,0.037060786,0.00007497039],"about_ca_topic_score_codex":0.0029680987,"about_ca_topic_score_gemma":0.0020260718,"teacher_disagreement_score":0.09068739,"about_ca_system_score_codex":0.0038999652,"about_ca_system_score_gemma":0.0038972264,"threshold_uncertainty_score":0.47960645},"labels":[],"label_agreement":null},{"id":"W2045029458","doi":"10.1080/08982110701456792","title":"Parameters Estimation for Condition Based Maintenance with Uncorrelated and Correlated Observations","year":2007,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","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":"Polytechnique Montréal","funders":"","keywords":"Uncorrelated; Hidden Markov model; Imperfect; Condition-based maintenance; Maximization; Process (computing); Markov chain; Computer science; Reliability engineering; Markov process; Engineering; Statistics; Econometrics; Mathematics; Mathematical optimization; Artificial intelligence; Machine learning","score_opus":0.018765607157707,"score_gpt":0.2853623261295281,"score_spread":0.26659671897182113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045029458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055873223,0.00012268528,0.9432768,0.00004008654,0.000008070362,0.000023735589,0.000051610357,0.00029183543,0.0003119415],"genre_scores_gemma":[0.8517508,0.000113290414,0.14704607,0.000021081865,0.000013177252,0.000055995602,0.00030106027,0.00005194268,0.0006465684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994906,0.00018430717,0.000029598858,0.00012103261,0.00013255172,0.00004198906],"domain_scores_gemma":[0.99624324,0.0030289353,0.0002420304,0.00020348142,0.00024415908,0.000038128375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016172172,0.00047964454,0.0006634349,0.00061276654,0.0002029765,0.00049858936,0.0005576592,0.00090041274,0.00082569406],"category_scores_gemma":[0.008982838,0.00040145862,0.0004916061,0.00046636327,0.00034465795,0.00065791316,0.00051378337,0.0006485364,0.00032663252],"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":[0.00025763403,0.00009383169,0.0037702024,0.00008076256,0.00008912106,0.0001193632,0.00012108943,0.8549938,0.006513667,0.0033980883,0.00044341537,0.13011898],"study_design_scores_gemma":[0.000008106327,0.000017997354,0.0010465226,0.000004440101,0.000007710689,0.00002751024,0.000005047899,0.9962197,0.0011678847,0.0013675261,0.00012011865,0.0000075066637],"about_ca_topic_score_codex":0.004810506,"about_ca_topic_score_gemma":0.0027316837,"teacher_disagreement_score":0.004810506,"about_ca_system_score_codex":0.00039520918,"about_ca_system_score_gemma":0.0005537157,"threshold_uncertainty_score":0.009564996},"labels":[],"label_agreement":null},{"id":"W2048282256","doi":"10.1080/08982110108918696","title":"ADAPTIVE QUALITY-CONTROL STRATEGY FOR PROGRESSIVE REDUCTION OF THE AMOUNT OF INSPECTION REQUIRED","year":2001,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","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":"Université de Sherbrooke","funders":"","keywords":"Quality (philosophy); Control (management); Reduction (mathematics); Business; Operations management; Computer science; Manufacturing engineering; Reliability engineering; Engineering; Mathematics; Artificial intelligence","score_opus":0.1976488270353533,"score_gpt":0.4590450565231333,"score_spread":0.26139622948778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048282256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09334819,0.00022172809,0.89399016,0.0003102152,0.000110606314,0.00064652244,0.00010594123,0.0058693183,0.0053973375],"genre_scores_gemma":[0.6762607,0.00010658653,0.31613624,0.00043639063,0.00005853742,0.0006914409,0.00020068916,0.0002163976,0.0058930577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803394,0.00038747088,0.00012228025,0.00042316434,0.00090262695,0.00013045198],"domain_scores_gemma":[0.9919756,0.003005354,0.00058814115,0.0008292317,0.0032704456,0.00033122054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026282158,0.0009457339,0.0006436267,0.0013944667,0.00037812462,0.00101367,0.0019685735,0.0008328465,0.007329232],"category_scores_gemma":[0.010556016,0.0003058973,0.00028348496,0.0006315368,0.00050763605,0.00079009816,0.0008476941,0.0008121058,0.0011625976],"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":[0.0016748625,0.001340634,0.008859913,0.0003054717,0.00007732651,0.00017098273,0.00027818716,0.03423752,0.14468494,0.0049713757,0.006808427,0.7965904],"study_design_scores_gemma":[0.00069793256,0.0014907173,0.018771466,0.000058666617,0.000111737914,0.00043127808,0.00012580596,0.8399745,0.12461449,0.005217783,0.008366557,0.00013907865],"about_ca_topic_score_codex":0.004226493,"about_ca_topic_score_gemma":0.003664664,"teacher_disagreement_score":0.007329232,"about_ca_system_score_codex":0.0007802771,"about_ca_system_score_gemma":0.0011055702,"threshold_uncertainty_score":0.024518728},"labels":[],"label_agreement":null},{"id":"W2053479559","doi":"10.1080/08982110108918693","title":"PROBABILISTIC ROBUST DESIGN WITH MULTIPLE QUALITY CHARACTERISTICS","year":2001,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quality (philosophy); Reliability engineering; Probabilistic logic; Computer science; Statistics; Mathematics; Engineering","score_opus":0.3321672477924726,"score_gpt":0.4397579413038509,"score_spread":0.10759069351137829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053479559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035579654,0.000052121766,0.9957776,0.00005096771,0.000006762522,0.00004556819,0.000021262513,0.000069908776,0.00041781322],"genre_scores_gemma":[0.6840838,0.00034544984,0.3097736,0.00016355349,0.000077950215,0.0009961496,0.00022963669,0.00012747986,0.0042024045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9872542,0.0050451322,0.0003966938,0.0023242605,0.004457391,0.00052236],"domain_scores_gemma":[0.9806594,0.011841537,0.0036591792,0.0022941094,0.0013193777,0.0002263665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01170205,0.002325103,0.0021212022,0.0014003436,0.0005062982,0.0019109114,0.0031799509,0.0022466646,0.0046810983],"category_scores_gemma":[0.028382853,0.0015906881,0.0018287183,0.001398376,0.002712629,0.003941964,0.0033469158,0.0019007201,0.00074464],"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":[0.0003760181,0.0001233581,0.0007522744,0.00028189007,0.00014498172,0.00005235704,0.00005956846,0.8688985,0.006387348,0.07256988,0.00032001038,0.05003374],"study_design_scores_gemma":[0.00008289941,0.00027173245,0.00061838445,0.000029010956,0.000062307365,0.000044966957,0.000010764429,0.94962305,0.0043929094,0.043934572,0.00089310197,0.00003630314],"about_ca_topic_score_codex":0.0007567306,"about_ca_topic_score_gemma":0.00067041087,"teacher_disagreement_score":0.01170205,"about_ca_system_score_codex":0.0020546839,"about_ca_system_score_gemma":0.0012771544,"threshold_uncertainty_score":0.061887085},"labels":[],"label_agreement":null},{"id":"W2058826455","doi":"10.1080/08982112.2013.846065","title":"Discussion of “The Statistical Evaluation of Categorical Measurements: ‘Simple Scales, but Treacherous Complexity Underneath’”","year":2013,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Categorical variable; Simple (philosophy); Econometrics; Image (mathematics); Statistics; Data science; Computer science; Mathematics; Artificial intelligence; Industrial engineering; Operations research; Engineering; Epistemology; Philosophy","score_opus":0.47012593719281637,"score_gpt":0.4759196666216318,"score_spread":0.005793729428815453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058826455","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005693853,0.0027431706,0.10617936,0.8342787,0.04760549,0.00025096556,0.00045034397,0.0009780438,0.006944585],"genre_scores_gemma":[0.012396639,0.002714441,0.12405506,0.7972802,0.04053696,0.0013727292,0.00033290914,0.0012312032,0.020079955],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92841303,0.044493992,0.0070952433,0.0034332825,0.015700314,0.00086407835],"domain_scores_gemma":[0.6341121,0.29051822,0.0061555975,0.01075377,0.05395908,0.004501283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11900072,0.0017168206,0.0014638919,0.003083682,0.00375933,0.0056446325,0.0072383676,0.014890764,0.022349278],"category_scores_gemma":[0.37273836,0.0015464232,0.003254492,0.0025231098,0.012204469,0.010187096,0.0054861177,0.02708008,0.0124891875],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052002568,0.000018386354,0.0002965373,0.00033733275,0.000034722343,0.0001376101,0.00091052527,0.00022049838,0.0006696863,0.030448997,0.93411577,0.03275787],"study_design_scores_gemma":[0.000052896117,0.00004799007,0.0012353907,0.0013137284,0.000044644552,0.0005598667,0.0007360232,0.0013101926,0.0012214091,0.065641165,0.9277258,0.00011089399],"about_ca_topic_score_codex":0.0061013894,"about_ca_topic_score_gemma":0.007825276,"teacher_disagreement_score":0.11900072,"about_ca_system_score_codex":0.0033739223,"about_ca_system_score_gemma":0.00693018,"threshold_uncertainty_score":0.6293435},"labels":[],"label_agreement":null},{"id":"W2061780373","doi":"10.1080/08982110108918668","title":"IMPLEMENTATION OF A COMPUTER-BASED MONITORING LOOP FOR QUALITY CONTROL OF PRODUCTION SYSTEMS","year":2001,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","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":"Université de Moncton","funders":"","keywords":"Quality (philosophy); Control (management); Loop (graph theory); Production (economics); Control chart; Computer science; Control system; Control engineering; Engineering; Manufacturing engineering; Operating system; Artificial intelligence; Process (computing)","score_opus":0.03034907667859361,"score_gpt":0.3039911484485373,"score_spread":0.2736420717699437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061780373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03039656,0.00030799714,0.95348865,0.00013054909,0.00018086734,0.0005249794,0.00011258597,0.011384001,0.0034738302],"genre_scores_gemma":[0.4630108,0.00021202597,0.53131896,0.00022943335,0.00010678946,0.0006166897,0.00032445468,0.00022141602,0.003959388],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904245,0.00016879117,0.00003934959,0.00017336273,0.0005054431,0.00007068027],"domain_scores_gemma":[0.99777824,0.000935698,0.0001491674,0.00031849276,0.0007411227,0.00007729181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011810698,0.00047734828,0.00045694422,0.00066175574,0.00040189823,0.0010666883,0.0013131184,0.0007010992,0.002714722],"category_scores_gemma":[0.0030439992,0.00028095144,0.00021145832,0.00034327595,0.0004092597,0.0005812902,0.00034476805,0.0009986707,0.0005958288],"study_design_candidate":"bench_or_experimental","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":[0.0011813181,0.0009988972,0.0031594,0.00047250488,0.00008689513,0.00021913668,0.00026794832,0.039612167,0.18886824,0.009681797,0.0059571583,0.7494947],"study_design_scores_gemma":[0.0005066952,0.0040442715,0.0072924006,0.000116764175,0.00014708746,0.0006717193,0.000051227427,0.49613282,0.42732084,0.004856755,0.058668274,0.00019107429],"about_ca_topic_score_codex":0.002322242,"about_ca_topic_score_gemma":0.0016019753,"teacher_disagreement_score":0.002714722,"about_ca_system_score_codex":0.0005838508,"about_ca_system_score_gemma":0.0010810596,"threshold_uncertainty_score":0.009081662},"labels":[],"label_agreement":null},{"id":"W2063319915","doi":"10.1081/qen-200059867","title":"Tracking Classroom Teaching and Learning: An SPC Application","year":2005,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Tracking (education); Mathematics education; Computer science; Business; Marketing; Manufacturing engineering; Industrial engineering; Operations management; Engineering; Psychology; Pedagogy","score_opus":0.011999493416362993,"score_gpt":0.27312386011749323,"score_spread":0.26112436670113026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063319915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07922134,0.0000610117,0.90208,0.00037321536,0.00007258085,0.0013028291,0.000562867,0.013989938,0.0023362262],"genre_scores_gemma":[0.50461984,0.00007846331,0.49190888,0.00010414375,0.000033501565,0.0015685383,0.00041523654,0.00023142439,0.0010400555],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99187005,0.0042380723,0.0004754659,0.00082572905,0.002460211,0.00013050945],"domain_scores_gemma":[0.96219814,0.025899012,0.0014256708,0.003820773,0.0060344683,0.00062185107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010192084,0.0007975716,0.0010145768,0.0023927335,0.00064059685,0.001792609,0.001771382,0.0016222215,0.003551908],"category_scores_gemma":[0.029674726,0.00040661148,0.0005147012,0.0030357002,0.0010459597,0.001480618,0.0015235535,0.001152128,0.00084327086],"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":[0.0013185391,0.0021745225,0.049890064,0.00058809895,0.00025319125,0.000466945,0.001362081,0.14539137,0.018355757,0.013949246,0.007296792,0.7589534],"study_design_scores_gemma":[0.0001309639,0.0008010666,0.006360507,0.000043452084,0.000041220115,0.00012663893,0.00015502133,0.9792753,0.007441823,0.0030573965,0.0025138306,0.000052841227],"about_ca_topic_score_codex":0.006622777,"about_ca_topic_score_gemma":0.0026364895,"teacher_disagreement_score":0.010192084,"about_ca_system_score_codex":0.0014155984,"about_ca_system_score_gemma":0.0015238225,"threshold_uncertainty_score":0.053901494},"labels":[],"label_agreement":null},{"id":"W2070081474","doi":"10.1081/qen-120018044","title":"Quality Improvement Strategy in the Electricity Supply Industry","year":2003,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Energy Efficiency and Management","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Connaught Fund; Division of Mathematical Sciences; University of Patras","keywords":"Taguchi methods; Quality (philosophy); Statistical process control; Mains electricity; Quality management; Total quality management; Electricity; Reliability engineering; Process capability; Engineering; Process (computing); Process capability index; Manufacturing engineering; Voltage; Operations management; Computer science; Work in process; Management system; Electrical engineering","score_opus":0.025885518711936447,"score_gpt":0.28081910343112587,"score_spread":0.2549335847191894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070081474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31829387,0.011850405,0.54762053,0.0129062785,0.0003241103,0.00038685877,0.00007068645,0.00070214935,0.107845105],"genre_scores_gemma":[0.9431663,0.0026039304,0.04434947,0.00048671837,0.00007744907,0.0000567175,0.000044190594,0.000025252555,0.009189919],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984394,0.00043537354,0.00006707891,0.00016942639,0.0007655653,0.00012314295],"domain_scores_gemma":[0.9993168,0.00013943976,0.00012891678,0.00004438358,0.00031468787,0.000055739925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012267955,0.00025698438,0.00017804927,0.000965353,0.00048439705,0.0019350484,0.00046743627,0.0005326038,0.00089617516],"category_scores_gemma":[0.0014457137,0.00008229633,0.00015014103,0.0010620928,0.0006330829,0.0010178912,0.000802937,0.00034654734,0.00017868023],"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":[0.000061324055,0.00024412095,0.0064347675,0.00043104438,0.00004504824,0.00037923048,0.0014398324,0.02000798,0.013685799,0.17012574,0.006469247,0.78067595],"study_design_scores_gemma":[0.0003050457,0.003685566,0.03758966,0.0010315096,0.00022133027,0.0017325794,0.0070755235,0.24778819,0.0496479,0.23536216,0.41540912,0.00015151808],"about_ca_topic_score_codex":0.0024306735,"about_ca_topic_score_gemma":0.0023655219,"teacher_disagreement_score":0.0024306735,"about_ca_system_score_codex":0.0016617491,"about_ca_system_score_gemma":0.0020887146,"threshold_uncertainty_score":0.012056887},"labels":[],"label_agreement":null},{"id":"W2080669340","doi":"10.1080/08982112.2012.641151","title":"Statistical Engineering—Roles for Statisticians and the Path Forward","year":2012,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistics Education and Methodologies","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":"University of Waterloo","funders":"","keywords":"Path (computing); Government (linguistics); Marketing; Management science; Engineering; Computer science; Data science; Operations research; Engineering management; Business","score_opus":0.14978279559090338,"score_gpt":0.43051357953853503,"score_spread":0.2807307839476316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080669340","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006420373,0.01926458,0.009953918,0.9641052,0.003931131,0.000017157052,0.000027043105,0.00011514828,0.00194379],"genre_scores_gemma":[0.15173692,0.12891531,0.15730509,0.5144609,0.035936113,0.0005020301,0.00020593354,0.00077906693,0.010158686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89216936,0.07093864,0.0062321536,0.0067379544,0.018627549,0.005294353],"domain_scores_gemma":[0.39710107,0.44690457,0.009887143,0.017219799,0.06891832,0.05996917],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2544509,0.0016936194,0.002571417,0.0052668247,0.010335096,0.027427157,0.0058297846,0.026432795,0.010672499],"category_scores_gemma":[0.27033868,0.0015405032,0.001372602,0.0046376246,0.04440909,0.044366036,0.014545992,0.05516421,0.004276275],"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":[0.00027780826,0.0004612529,0.0031542566,0.0011341551,0.00008909389,0.00038351546,0.007885097,0.0010684576,0.0009564467,0.55345,0.23400593,0.1971341],"study_design_scores_gemma":[0.00014727689,0.0002984874,0.0012406163,0.002546104,0.00002362848,0.00048746346,0.012085674,0.0019179634,0.00033068986,0.69438577,0.28633744,0.00019884],"about_ca_topic_score_codex":0.0047645606,"about_ca_topic_score_gemma":0.004806326,"teacher_disagreement_score":0.2544509,"about_ca_system_score_codex":0.007615403,"about_ca_system_score_gemma":0.044741746,"threshold_uncertainty_score":0.9193948},"labels":[],"label_agreement":null},{"id":"W2093077824","doi":"10.1080/08982110802247744","title":"Evaluating Three DOE Methodologies: Optimization of a Composite Laminate under Fabrication Error","year":2008,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; National Science Foundation","keywords":"Design of experiments; Response surface methodology; Computer science; Composite number; Bayesian probability; Reliability engineering; Engineering drawing; Engineering; Mathematics; Algorithm; Machine learning; Artificial intelligence; Statistics","score_opus":0.20182623593448912,"score_gpt":0.41174375457916007,"score_spread":0.20991751864467095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093077824","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50134933,0.0007003282,0.49542066,0.00006706133,0.000022492228,0.00016114107,0.0000754341,0.00014498875,0.0020585402],"genre_scores_gemma":[0.7530887,0.00021054325,0.24605015,0.000015243936,0.0000045439665,0.00018690457,0.0000480454,0.000027131076,0.00036861177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741346,0.0015728264,0.00007493798,0.00015780731,0.0007033769,0.00007758978],"domain_scores_gemma":[0.9944015,0.004269741,0.00041251312,0.00026926456,0.0005984299,0.000048582406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00546768,0.0008698956,0.00076090737,0.000847296,0.00028679147,0.0006646234,0.00035569404,0.00065152836,0.00037654076],"category_scores_gemma":[0.0074307746,0.0003093657,0.00071500445,0.00060666347,0.00047271381,0.0004138016,0.00040500177,0.00040607012,0.000050813695],"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":[0.00033112415,0.0001913182,0.0018898373,0.00023633796,0.000115498704,0.00004403648,0.00007720833,0.90504473,0.03466447,0.002164303,0.00008406293,0.055157043],"study_design_scores_gemma":[0.000072624694,0.0014282333,0.002347036,0.000026236181,0.00007384642,0.00004924541,0.00006278889,0.9343357,0.05969132,0.0010536626,0.0008214452,0.000037770013],"about_ca_topic_score_codex":0.0011483808,"about_ca_topic_score_gemma":0.0014743346,"teacher_disagreement_score":0.00546768,"about_ca_system_score_codex":0.0006209367,"about_ca_system_score_gemma":0.00072939484,"threshold_uncertainty_score":0.02891624},"labels":[],"label_agreement":null},{"id":"W2093956415","doi":"10.1080/08982110601093679","title":"Imputation of Censored Response Data in a Bivariate Designed Experiment","year":2006,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pacific Institute for the Mathematical Sciences; National Science Foundation","keywords":"Bivariate analysis; Imputation (statistics); Bivariate data; Statistics; Multivariate statistics; Econometrics; Multivariate normal distribution; Computer science; Missing data; Data mining; Mathematics","score_opus":0.20312244162418633,"score_gpt":0.4854396034963143,"score_spread":0.28231716187212796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093956415","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017974002,0.00023131607,0.98060554,0.00017560214,0.00007088826,0.00021045023,0.00014579047,0.00014536438,0.0004409895],"genre_scores_gemma":[0.34607598,0.0005777183,0.65011984,0.00039783967,0.000067898836,0.0016871332,0.0002877452,0.00006116822,0.00072468404],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90246654,0.08399447,0.002996346,0.0034581465,0.0062518995,0.00083250215],"domain_scores_gemma":[0.81202525,0.1503958,0.012741354,0.01898269,0.005058407,0.00079653866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.074060366,0.00081094616,0.0030901164,0.0012159363,0.0005646225,0.0016651398,0.002182652,0.0016536919,0.0027266329],"category_scores_gemma":[0.1551896,0.00059564656,0.0018918692,0.0018563044,0.0018930204,0.0012274021,0.0016562776,0.002515331,0.00045228223],"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":[0.009564274,0.0020170035,0.04778463,0.004293773,0.0030125964,0.0013813154,0.00175962,0.20535524,0.031575564,0.20297413,0.004439241,0.48584256],"study_design_scores_gemma":[0.0014427352,0.007679929,0.032058172,0.0016967505,0.0019302258,0.0009753283,0.0004277144,0.5772571,0.075051546,0.28627264,0.014743677,0.0004642557],"about_ca_topic_score_codex":0.00040136007,"about_ca_topic_score_gemma":0.00037765788,"teacher_disagreement_score":0.074060366,"about_ca_system_score_codex":0.000832043,"about_ca_system_score_gemma":0.0016683812,"threshold_uncertainty_score":0.39167333},"labels":[],"label_agreement":null},{"id":"W2135885901","doi":"","title":"On sample allocation for efficient domain estimation","year":2013,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":24,"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":"Statistics; Sample (material); Estimator; Sample size determination; Population; Econometrics; Context (archaeology); Simple random sample; Stratified sampling; Stratum; Mathematics; Reliability (semiconductor); Estimation; Sampling design; Survey sampling; Aggregate (composite); Computer science; Geography; Economics; Engineering; Demography","score_opus":0.08950316587032563,"score_gpt":0.3695666906709428,"score_spread":0.28006352480061714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135885901","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006642596,0.0001102435,0.99828184,0.00012901389,0.00001783373,0.00016475873,0.000023890812,0.00006594753,0.0005422749],"genre_scores_gemma":[0.025289096,0.00032268555,0.97125185,0.00018545866,0.00010077925,0.001977884,0.00013707193,0.00008850814,0.00064661895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91342956,0.07660911,0.0016367503,0.0032080384,0.004389898,0.0007267754],"domain_scores_gemma":[0.7908676,0.18363975,0.0034863257,0.016152302,0.0053177006,0.0005362452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07742239,0.0022128695,0.0038419727,0.0042742696,0.0016272019,0.0023886191,0.004023411,0.0026265138,0.0060529043],"category_scores_gemma":[0.2606179,0.0017376746,0.002146182,0.006339868,0.004629883,0.0042150235,0.007695396,0.00487922,0.0022696261],"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":[0.00046756657,0.00019140891,0.0023589265,0.0006066984,0.0002570817,0.00019761299,0.00091726216,0.13984735,0.0022088906,0.54004407,0.0047187144,0.30818444],"study_design_scores_gemma":[0.0002471304,0.0002232737,0.0009334852,0.00024560542,0.00008852118,0.00018588,0.00014169082,0.48924032,0.0021372745,0.4965679,0.009929055,0.000059818674],"about_ca_topic_score_codex":0.0023503352,"about_ca_topic_score_gemma":0.0017120709,"teacher_disagreement_score":0.07742239,"about_ca_system_score_codex":0.0020812727,"about_ca_system_score_gemma":0.0034976702,"threshold_uncertainty_score":0.40945363},"labels":[],"label_agreement":null},{"id":"W2138725277","doi":"10.1080/08982110701241293","title":"Probability Constrained Optimization as a Tool for Functional Design for Six Sigma","year":2007,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Waterloo","funders":"University of Waterloo","keywords":"Robustness (evolution); Six Sigma; Reliability engineering; Probabilistic design; Mathematical optimization; Design of experiments; Engineering; Computer science; Mathematics; Engineering design process; Manufacturing engineering; Statistics","score_opus":0.3031025173730075,"score_gpt":0.45388115307334037,"score_spread":0.1507786357003329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138725277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017352403,0.00008401547,0.99686974,0.00006448893,0.000013191352,0.00008775774,0.0000111791915,0.00009910361,0.0010353166],"genre_scores_gemma":[0.18063699,0.0002730572,0.81658375,0.00013162178,0.000030497149,0.0011764014,0.00008103872,0.00010332048,0.0009834063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944804,0.003829329,0.00018857236,0.00024040602,0.0011302037,0.00013114701],"domain_scores_gemma":[0.99163204,0.0068559865,0.00045434714,0.0002492995,0.0007379933,0.00007025878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011574166,0.0018329804,0.0016140098,0.0019428171,0.0006044605,0.0012983185,0.0012231837,0.0012489937,0.004353618],"category_scores_gemma":[0.012115843,0.0009089317,0.0013132853,0.0011019496,0.001705306,0.0008129896,0.0014710439,0.0019962871,0.00042315098],"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":[0.000077962075,0.0000584232,0.00022930941,0.00016667445,0.00006600025,0.00005129351,0.000056749668,0.91619706,0.0008340079,0.05732115,0.00031100633,0.024630316],"study_design_scores_gemma":[0.000034538945,0.00011153669,0.000052127194,0.000033641692,0.000013640311,0.000008183801,0.000010557397,0.97894204,0.0005608096,0.01903937,0.0011831102,0.000010522257],"about_ca_topic_score_codex":0.0020107601,"about_ca_topic_score_gemma":0.0013200451,"teacher_disagreement_score":0.011574166,"about_ca_system_score_codex":0.001354948,"about_ca_system_score_gemma":0.0021795875,"threshold_uncertainty_score":0.06121075},"labels":[],"label_agreement":null},{"id":"W2276203798","doi":"","title":"Design effects for the weighted mean and total estimators under complex survey sampling","year":2006,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Estimator; Statistics; Sampling (signal processing); Weighted arithmetic mean; Mathematics; Survey research; Sampling design; Econometrics; Computer science; Psychology; Demography; Telecommunications; Sociology; Applied psychology","score_opus":0.24238952622116794,"score_gpt":0.39020836300012235,"score_spread":0.1478188367789544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276203798","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046029785,0.0009181499,0.986479,0.0016919884,0.00018453998,0.00017269443,0.000091768066,0.00008883666,0.0057702027],"genre_scores_gemma":[0.23204334,0.003985008,0.75160164,0.002561469,0.00089862966,0.0034338918,0.00019252543,0.0003094645,0.004973955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.84410024,0.13022931,0.0038855772,0.0071971053,0.013124422,0.0014632855],"domain_scores_gemma":[0.33636987,0.6069384,0.020349199,0.024557084,0.010926719,0.0008586349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16927877,0.0013513776,0.0021411525,0.003493632,0.00096783944,0.0032062377,0.003867054,0.0035755103,0.013098531],"category_scores_gemma":[0.49599677,0.0010925386,0.0029622838,0.0046230713,0.009479353,0.010012081,0.0054670833,0.008434582,0.0012715765],"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":[0.00006658118,0.000024713796,0.0015709847,0.00031343068,0.00009420485,0.000051524483,0.00030121955,0.0057921424,0.00017965402,0.947713,0.0014827834,0.042409666],"study_design_scores_gemma":[0.00010767989,0.0002925896,0.0019461645,0.00040309646,0.00026608896,0.00018581071,0.00009827732,0.04595529,0.0009521545,0.9402288,0.009500869,0.000063095555],"about_ca_topic_score_codex":0.0012923871,"about_ca_topic_score_gemma":0.0013816449,"teacher_disagreement_score":0.16927877,"about_ca_system_score_codex":0.0028753374,"about_ca_system_score_gemma":0.002155215,"threshold_uncertainty_score":0.8952424},"labels":[],"label_agreement":null},{"id":"W2290566946","doi":"","title":"A general criterion for factorial designs under model uncertainty","year":2010,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"","keywords":"Generalization; Computer science; Mathematical optimization; Limiting; Factorial experiment; Mathematics; Machine learning; Engineering","score_opus":0.3655784518244831,"score_gpt":0.5189059344658788,"score_spread":0.15332748264139573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290566946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001864852,0.00016328396,0.99657416,0.00014825207,0.000019716861,0.00012494903,0.00008371267,0.00014718187,0.0008738998],"genre_scores_gemma":[0.06766294,0.00027904467,0.92847365,0.00043766873,0.000072252646,0.0013349117,0.0003371944,0.00025530672,0.0011470574],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9360444,0.04218673,0.003123111,0.0041032755,0.013657837,0.00088463415],"domain_scores_gemma":[0.8818491,0.08731228,0.0056924615,0.007994823,0.01595786,0.0011935115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.068426706,0.0022447587,0.0030682494,0.004324836,0.0012467562,0.0025250185,0.0027051342,0.003125064,0.0048276093],"category_scores_gemma":[0.16566932,0.0011498716,0.0017315231,0.003793877,0.0039024279,0.0034304024,0.0037672513,0.002889044,0.0015602302],"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":[0.0005803849,0.00015579132,0.0029534618,0.0016528275,0.00032187538,0.00019254674,0.00047882326,0.19926542,0.013101812,0.5620107,0.0058779893,0.21340843],"study_design_scores_gemma":[0.00021789724,0.0008554175,0.0022671313,0.00027778413,0.00009309307,0.0002644567,0.0000847108,0.37365437,0.006757687,0.5995519,0.015832033,0.0001435089],"about_ca_topic_score_codex":0.0018249968,"about_ca_topic_score_gemma":0.0013439193,"teacher_disagreement_score":0.068426706,"about_ca_system_score_codex":0.0032155807,"about_ca_system_score_gemma":0.006203452,"threshold_uncertainty_score":0.36187935},"labels":[],"label_agreement":null},{"id":"W2530745759","doi":"","title":"Combined composite likelihood","year":2015,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Pairwise comparison; Quasi-maximum likelihood; Likelihood function; Likelihood principle; Constant (computer programming); Conditional independence; Range (aeronautics); Econometrics; Statistics; Mathematics; Maximum likelihood; Independence (probability theory); Composite number; Restricted maximum likelihood; Marginal likelihood; Value (mathematics); Function (biology); Computer science; Engineering; Algorithm","score_opus":0.12398426855734868,"score_gpt":0.39474499573105165,"score_spread":0.270760727173703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530745759","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00246153,0.0007853422,0.9864703,0.000510085,0.000125146,0.00006987613,0.00058585516,0.0004888961,0.00850302],"genre_scores_gemma":[0.16965482,0.0013912545,0.807916,0.000662075,0.0006468525,0.00049824425,0.0023175993,0.0012092848,0.015703872],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9895174,0.0060599903,0.0004121341,0.0015212977,0.0021131705,0.00037604768],"domain_scores_gemma":[0.9621709,0.023874903,0.0015683731,0.006382092,0.005033012,0.0009706595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012968024,0.0017113147,0.003078595,0.0039000395,0.001124624,0.006368209,0.004127926,0.0026680976,0.02448659],"category_scores_gemma":[0.058308598,0.000877847,0.0028734133,0.0050767437,0.0021258206,0.008069324,0.006020188,0.0052260226,0.006653591],"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":[0.0006310183,0.00016978836,0.006277085,0.0010607867,0.0007671009,0.0005545079,0.00041912877,0.08795041,0.0027345957,0.5617548,0.027891679,0.30978915],"study_design_scores_gemma":[0.00006650572,0.00017643931,0.0023823625,0.00024413916,0.00024530047,0.0010047377,0.00015286014,0.3313493,0.0024152335,0.6171752,0.044638813,0.00014917311],"about_ca_topic_score_codex":0.0018719452,"about_ca_topic_score_gemma":0.0030776763,"teacher_disagreement_score":0.02448659,"about_ca_system_score_codex":0.0019770523,"about_ca_system_score_gemma":0.0034176267,"threshold_uncertainty_score":0.081915855},"labels":[],"label_agreement":null},{"id":"W2743279021","doi":"10.1080/08982112.2017.1358367","title":"Optimum variable-dimension EWMA chart for multivariate statistical process control","year":2017,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":9,"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":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Wilfrid Laurier University","keywords":"EWMA chart; Control chart; Chart; Shewhart individuals control chart; X-bar chart; Markov chain; Statistical process control; Statistics; Control limits; Computer science; Software; Process (computing); Mathematics","score_opus":0.14783668588648227,"score_gpt":0.48814303912629337,"score_spread":0.34030635323981107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743279021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031577602,0.000112184876,0.99580353,0.000028163215,0.000017091841,0.000029777902,0.00003509641,0.00043333924,0.00038292745],"genre_scores_gemma":[0.25789064,0.00031471043,0.73975664,0.00006908189,0.00007513501,0.00032855076,0.00030836623,0.00023735322,0.0010195223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671566,0.0016209895,0.00017368559,0.0005134075,0.00080739387,0.00016889696],"domain_scores_gemma":[0.99417084,0.0035696148,0.0004731786,0.00032115978,0.001351742,0.000113338574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004989156,0.0011195063,0.0012504458,0.00128828,0.0006010163,0.0015993485,0.0009157158,0.00092516624,0.002440094],"category_scores_gemma":[0.013063387,0.00043444638,0.0009030909,0.0012215483,0.0007883786,0.0011874088,0.0006888079,0.001523911,0.00045102378],"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":[0.00033165922,0.00007748946,0.0010846328,0.00019688782,0.000068328794,0.000082832375,0.000095691816,0.8150108,0.010024725,0.031528875,0.0022620205,0.13923599],"study_design_scores_gemma":[0.0000064615383,0.000022387763,0.000113302194,0.000004089682,0.0000039276783,0.0000064090414,0.0000016604956,0.99642545,0.0014987894,0.0015008263,0.0004070524,0.000009710117],"about_ca_topic_score_codex":0.0053351903,"about_ca_topic_score_gemma":0.002870655,"teacher_disagreement_score":0.0053351903,"about_ca_system_score_codex":0.001103603,"about_ca_system_score_gemma":0.0017714081,"threshold_uncertainty_score":0.026385546},"labels":[],"label_agreement":null},{"id":"W293502839","doi":"","title":"Robust generalized regression estimation","year":2005,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Estimator; Outlier; Robustness (evolution); Mathematics; Minimum-variance unbiased estimator; Robust statistics; Least absolute deviations; Mean squared error; Efficient estimator; Population; Statistics; Mathematical optimization; Econometrics","score_opus":0.24970167006956667,"score_gpt":0.4612575668067697,"score_spread":0.21155589673720304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W293502839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001580314,0.00042453458,0.99547064,0.00013584517,0.000053380936,0.000050993607,0.000245311,0.00052922615,0.0015097364],"genre_scores_gemma":[0.16402188,0.0015153327,0.82102704,0.00044036875,0.0003622713,0.00056504627,0.002480293,0.0006950954,0.008892623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9911308,0.0053508426,0.00034640473,0.0016689528,0.0011728773,0.00033007894],"domain_scores_gemma":[0.98924047,0.0053959796,0.0011943678,0.002746479,0.0013063919,0.00011626833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077689164,0.0020303784,0.0036827945,0.0022688038,0.0005790113,0.0024091683,0.0036640428,0.0022002168,0.0077326726],"category_scores_gemma":[0.03434578,0.00078130356,0.00300559,0.0034145568,0.0009614263,0.0019526036,0.0025640677,0.0021735914,0.003502793],"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":[0.00018389199,0.0000818694,0.0043478045,0.0008207877,0.0012593935,0.00022656524,0.00017482636,0.4095527,0.002709245,0.21646753,0.015260237,0.34891513],"study_design_scores_gemma":[0.000046525016,0.00009860229,0.0016240004,0.00014795663,0.0001975518,0.00014536885,0.00006253005,0.86937934,0.001824796,0.1044944,0.021907886,0.00007103249],"about_ca_topic_score_codex":0.0044599054,"about_ca_topic_score_gemma":0.0037944014,"teacher_disagreement_score":0.0077689164,"about_ca_system_score_codex":0.0010760311,"about_ca_system_score_gemma":0.0018836384,"threshold_uncertainty_score":0.041086435},"labels":[],"label_agreement":null},{"id":"W2943253458","doi":"10.1080/08982112.2018.1548022","title":"Statistical reasoning in diagnostic problem-solving—The case of flow-rate measurements","year":2019,"lang":"en","type":"article","venue":"Quality Engineering","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Suspect; Hierarchy; Computer science; Domain (mathematical analysis); Statistical hypothesis testing; Statistical analysis; Flow (mathematics); Econometrics; Management science; Statistics; Mathematics; Engineering; Psychology; Economics","score_opus":0.026909854212799483,"score_gpt":0.2714776724664599,"score_spread":0.2445678182536604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943253458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020515447,0.0010045511,0.96080875,0.011040509,0.000109320215,0.00017770502,0.00012700248,0.00036379654,0.0058529577],"genre_scores_gemma":[0.31648067,0.00052747363,0.68063956,0.0009197334,0.00014703172,0.0002683854,0.00014625094,0.000121134646,0.0007498028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.94651717,0.0375949,0.003031283,0.0045280256,0.006975905,0.0013526434],"domain_scores_gemma":[0.72433907,0.24957605,0.008602546,0.009339681,0.0064989505,0.0016436168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04375805,0.0015726404,0.0017415747,0.0057918,0.0031583793,0.0065783695,0.0047681774,0.005843971,0.003587386],"category_scores_gemma":[0.19368875,0.0016072807,0.0033984918,0.004627163,0.020781996,0.013903601,0.005454208,0.006686071,0.0004312667],"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":[0.00021608997,0.00019362164,0.006293893,0.0009678111,0.00032098498,0.0013995589,0.0052638426,0.08536341,0.0012782285,0.8109375,0.0029596903,0.084805265],"study_design_scores_gemma":[0.00006717436,0.00005148075,0.0006645713,0.00012920579,0.00005277728,0.0002925436,0.0006748215,0.13024749,0.0013541523,0.8619605,0.0044521317,0.000053240394],"about_ca_topic_score_codex":0.007850216,"about_ca_topic_score_gemma":0.004481493,"teacher_disagreement_score":0.04375805,"about_ca_system_score_codex":0.0047554187,"about_ca_system_score_gemma":0.005324169,"threshold_uncertainty_score":0.23141748},"labels":[],"label_agreement":null},{"id":"W3022851209","doi":"","title":"Fitting regression models with response-biased samples","year":2013,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Methods and Models","field":"Mathematics","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":"Covariate; Mathematics; Statistics; Econometrics; Maximum likelihood; Generalized linear model; Humanities; Philosophy","score_opus":0.22365300552902354,"score_gpt":0.4306323652089237,"score_spread":0.20697935967990014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022851209","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05818008,0.00057055725,0.93824095,0.00069529016,0.000078994526,0.0001769992,0.0003631888,0.00063492084,0.0010589687],"genre_scores_gemma":[0.59389037,0.0008149113,0.39495394,0.001273933,0.00020827499,0.00086724194,0.0029368482,0.0004922683,0.004562321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9683224,0.022121172,0.0012609283,0.00466358,0.0028203994,0.0008116079],"domain_scores_gemma":[0.92178345,0.057375062,0.006237336,0.010996336,0.0030127978,0.0005950927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031922203,0.0015768748,0.003009216,0.0019695668,0.0007879287,0.0033970287,0.004322373,0.004278509,0.003663691],"category_scores_gemma":[0.1534056,0.001759126,0.0039425557,0.0026531504,0.0016095833,0.004472287,0.0032656295,0.004126331,0.0013255993],"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":[0.00073062297,0.00042505402,0.043829422,0.00078875467,0.0011960291,0.0006490031,0.001451559,0.69419026,0.0023267854,0.11156913,0.004081196,0.1387622],"study_design_scores_gemma":[0.00017102233,0.00014631706,0.0035845838,0.000124464,0.00011366172,0.00019132216,0.00021033279,0.8594732,0.0010611167,0.13075598,0.0040874775,0.00008053423],"about_ca_topic_score_codex":0.007205025,"about_ca_topic_score_gemma":0.005935634,"teacher_disagreement_score":0.031922203,"about_ca_system_score_codex":0.002039835,"about_ca_system_score_gemma":0.0019130809,"threshold_uncertainty_score":0.16882277},"labels":[],"label_agreement":null},{"id":"W3024827624","doi":"10.1080/08982112.2020.1741619","title":"Bayesian probability of agreement for comparing survival or reliability functions with parametric lifetime regression models","year":2020,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariate; Bayesian probability; Reliability (semiconductor); Weibull distribution; Parametric statistics; Similarity (geometry); Computer science; Statistics; Econometrics; Data mining; Mathematics; Artificial intelligence","score_opus":0.1977749977517763,"score_gpt":0.3806717568118881,"score_spread":0.1828967590601118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024827624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030075544,0.00045560446,0.9943738,0.0001911645,0.000062590014,0.000080156904,0.00013987388,0.00019439058,0.0014949184],"genre_scores_gemma":[0.3181706,0.0013296191,0.67383856,0.0006015131,0.00047031345,0.0022235971,0.0011749452,0.00074240763,0.0014484852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8807805,0.08789946,0.004625909,0.009418338,0.016180776,0.0010949917],"domain_scores_gemma":[0.6042699,0.34007877,0.018869625,0.024260167,0.011411063,0.0011104407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13590702,0.0019412136,0.0025862358,0.008094971,0.0014076995,0.005034673,0.004216637,0.004030596,0.005377478],"category_scores_gemma":[0.40431413,0.0012050627,0.0037423861,0.005228926,0.0071820854,0.0076613277,0.0058004307,0.0063883057,0.001313388],"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":[0.00046002734,0.00015195619,0.012572616,0.0012000771,0.0015766557,0.00024239482,0.001805552,0.20289415,0.0016459702,0.64098066,0.005540153,0.13092978],"study_design_scores_gemma":[0.000067508,0.00025709163,0.004875442,0.0004543237,0.00022497283,0.0003853156,0.0003441892,0.33808365,0.002012792,0.64485013,0.008251557,0.00019306861],"about_ca_topic_score_codex":0.002218946,"about_ca_topic_score_gemma":0.0011398596,"teacher_disagreement_score":0.13590702,"about_ca_system_score_codex":0.0025849505,"about_ca_system_score_gemma":0.002657819,"threshold_uncertainty_score":0.71875364},"labels":[],"label_agreement":null},{"id":"W3108099388","doi":"10.1080/08982112.2020.1786118","title":"A two-step method for monitoring normally distributed multi-stream processes in high dimensions","year":2020,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control chart; Computer science; False alarm; Constant false alarm rate; Statistical process control; Chart; Data mining; Data stream mining; Real-time computing; Algorithm; Statistics; Process (computing); Mathematics; Artificial intelligence","score_opus":0.17956043130302685,"score_gpt":0.46621712466755,"score_spread":0.28665669336452315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108099388","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026907977,0.00006304435,0.9965249,0.000019539746,0.000019271954,0.00004122379,0.000027467302,0.00041544635,0.00019844547],"genre_scores_gemma":[0.08258064,0.00013090963,0.9152677,0.00006134505,0.00004950072,0.00018510471,0.00015414349,0.00010186649,0.0014687947],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998657,0.00031563715,0.00006289986,0.00025125293,0.00065792777,0.000055212215],"domain_scores_gemma":[0.99759716,0.0010813442,0.00019852095,0.00022116408,0.0008110271,0.00009079551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014991137,0.00094069156,0.00083680125,0.0015467479,0.00036841945,0.00079161907,0.001010133,0.00067164865,0.0014670255],"category_scores_gemma":[0.0040048975,0.00034295637,0.0006598004,0.0007388664,0.0005776649,0.0009529382,0.0008390017,0.0012694491,0.0006325587],"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":[0.00061400805,0.00027885378,0.006392399,0.00042342482,0.00024319834,0.00021912421,0.00021187308,0.12884814,0.09202641,0.0141502395,0.004104344,0.75248796],"study_design_scores_gemma":[0.000028369628,0.00020923172,0.0022163847,0.0000119038095,0.00003065842,0.00017366973,0.000020106701,0.9687173,0.022976872,0.0026798828,0.002857939,0.00007773008],"about_ca_topic_score_codex":0.0018320403,"about_ca_topic_score_gemma":0.0020941494,"teacher_disagreement_score":0.0018320403,"about_ca_system_score_codex":0.0004212186,"about_ca_system_score_gemma":0.0010122181,"threshold_uncertainty_score":0.007928193},"labels":[],"label_agreement":null},{"id":"W3129859778","doi":"10.1080/08982112.2020.1814959","title":"Using simulation to handle implicit likelihoods in a Bayesian analysis","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Markov chain Monte Carlo; Bayesian probability; Computer science; Variable-order Bayesian network; Key (lock); Bayesian inference; Markov chain; Marginal likelihood; Econometrics; Data mining; Artificial intelligence; Machine learning; Mathematics","score_opus":0.054413614561321016,"score_gpt":0.37417425237494345,"score_spread":0.3197606378136224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129859778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000838373,0.000039014387,0.9985171,0.00016044726,0.0000093428835,0.00001206579,0.000008249887,0.000054250228,0.00036113808],"genre_scores_gemma":[0.09340169,0.0003284376,0.90405333,0.00018653105,0.00008866551,0.00020477676,0.00008739383,0.00022281922,0.0014263665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97491634,0.018970933,0.00073204323,0.0013004295,0.0037995838,0.0002807259],"domain_scores_gemma":[0.9027045,0.08449294,0.0027053019,0.0068545095,0.0026415677,0.00060123584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028075347,0.0014496109,0.0015703876,0.002923532,0.0015371821,0.0044950154,0.0033903471,0.0031738637,0.006966926],"category_scores_gemma":[0.13225628,0.0014884858,0.0018435268,0.0025192627,0.0061354567,0.008886714,0.007222759,0.0063806977,0.001242808],"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":[0.000052594834,0.000043977583,0.0015245638,0.00011625534,0.00009622504,0.00016743259,0.0003941182,0.18393242,0.0007575206,0.77291954,0.0007656048,0.039229747],"study_design_scores_gemma":[0.000018577086,0.000013184289,0.00012146463,0.00005518653,0.000013250085,0.000068199064,0.000026930313,0.40169933,0.00053848844,0.59497625,0.0024417762,0.000027352162],"about_ca_topic_score_codex":0.00339007,"about_ca_topic_score_gemma":0.003553949,"teacher_disagreement_score":0.028075347,"about_ca_system_score_codex":0.0019528591,"about_ca_system_score_gemma":0.003170524,"threshold_uncertainty_score":0.14847845},"labels":[],"label_agreement":null},{"id":"W3143231775","doi":"","title":"Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: An empirical study","year":2007,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Quality and Supply Management","field":"Business, Management and Accounting","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 Saskatchewan","funders":"","keywords":"Total quality management; Contingency theory; Contingency; Context (archaeology); Scope (computer science); Business; Argument (complex analysis); Organizational performance; Empirical research; Knowledge management; Quality (philosophy); Process management; Psychology; Marketing; Computer science; Statistics; Mathematics; Lean manufacturing","score_opus":0.04033840947664724,"score_gpt":0.2831673675143815,"score_spread":0.24282895803773424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143231775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99674785,0.00013262511,0.00075307954,0.00012402204,0.0000039448646,0.00006181455,0.000044484266,0.000004472624,0.0021277573],"genre_scores_gemma":[0.99929786,0.00007165576,0.00048989477,0.000023602584,0.000004378673,0.000034251185,0.000025350557,0.0000018824593,0.000051143692],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98993695,0.0066830777,0.00040837933,0.0006233847,0.0016011118,0.00074698875],"domain_scores_gemma":[0.9010148,0.075422205,0.012753131,0.0030759426,0.005026796,0.0027071382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009273712,0.00039029008,0.00052632904,0.002586888,0.0019493,0.003064132,0.0008888553,0.00082576176,0.003547105],"category_scores_gemma":[0.03752471,0.00040858553,0.00057744305,0.004279334,0.0034098432,0.0019886997,0.0030220842,0.0014464078,0.00022754398],"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":[0.0002142599,0.002426119,0.9441397,0.00031710725,0.00023667671,0.0003661757,0.014238581,0.0023382278,0.000594123,0.007854833,0.00039524504,0.026878905],"study_design_scores_gemma":[0.00004254667,0.0009031215,0.9449067,0.00018962915,0.00017108399,0.00009841359,0.04176344,0.00636477,0.00067954545,0.003007293,0.0018252021,0.00004815894],"about_ca_topic_score_codex":0.006875284,"about_ca_topic_score_gemma":0.009293504,"teacher_disagreement_score":0.009273712,"about_ca_system_score_codex":0.003512221,"about_ca_system_score_gemma":0.0037810814,"threshold_uncertainty_score":0.04904467},"labels":[],"label_agreement":null},{"id":"W3203362329","doi":"10.1080/08982112.2021.1938118","title":"Identifying dominant causes using leveraged study designs","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","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":"Exploit; Computer science; Process (computing); Plan (archaeology); Variation (astronomy); Data mining; Computer security","score_opus":0.5503989095260722,"score_gpt":0.5362244254218561,"score_spread":0.014174484104216067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203362329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028499465,0.0008690963,0.9635451,0.0005813596,0.00018711164,0.00464504,0.00018080497,0.00021230914,0.0012797373],"genre_scores_gemma":[0.4052761,0.0002877471,0.58429396,0.0003662338,0.00012084057,0.009105793,0.000121047495,0.000052270276,0.00037602917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.518294,0.40052873,0.023577323,0.024158753,0.031636477,0.0018046509],"domain_scores_gemma":[0.3010893,0.60381085,0.028540026,0.049067784,0.016299432,0.0011925993],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3574996,0.00235284,0.00415763,0.0090136025,0.0024072945,0.003578311,0.003407935,0.0038024075,0.004787507],"category_scores_gemma":[0.5026447,0.0011256717,0.0065491702,0.0037100718,0.005213467,0.0052995738,0.006139116,0.003663409,0.00037619454],"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":[0.00787999,0.001338984,0.08157867,0.008440337,0.021366173,0.0011685482,0.0072043235,0.030855823,0.010247272,0.2800468,0.0030312608,0.54684186],"study_design_scores_gemma":[0.0053081624,0.014372976,0.0314695,0.0024142799,0.011472987,0.0010151541,0.0020761278,0.25820747,0.023472082,0.6333832,0.016211782,0.0005962737],"about_ca_topic_score_codex":0.00079291535,"about_ca_topic_score_gemma":0.0010428514,"teacher_disagreement_score":0.3574996,"about_ca_system_score_codex":0.0024270953,"about_ca_system_score_gemma":0.006149135,"threshold_uncertainty_score":0.7923174},"labels":[],"label_agreement":null},{"id":"W3204480796","doi":"10.1080/08982112.2021.1982543","title":"In memory of Lai K. Chan","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","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 Manitoba; McGill University","funders":"","keywords":"Statistical analysis; Mathematics; Quality (philosophy); Statistics; Operations management; Agricultural science; Forestry; Agricultural economics; Advertising; Environmental science; Geography; Business; Engineering; Physics; Economics","score_opus":0.16073191687886135,"score_gpt":0.4722127426654625,"score_spread":0.31148082578660113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204480796","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010956941,0.038794383,0.0020576697,0.53063226,0.3899697,0.000055092096,0.0005337034,0.0002906087,0.03657085],"genre_scores_gemma":[0.021383079,0.044645745,0.0017452394,0.1565068,0.23354438,0.00012843258,0.0006516501,0.00038818177,0.54100645],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982151,0.00028188064,0.00012363605,0.0005103916,0.0007176333,0.0001513609],"domain_scores_gemma":[0.98939055,0.001641704,0.00072789413,0.00040024935,0.005373188,0.0024665084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023550908,0.00079077494,0.0008959468,0.0007026473,0.0012909552,0.0044104187,0.0011630169,0.0026512963,0.064051256],"category_scores_gemma":[0.020104827,0.00024327693,0.00043114132,0.00052466063,0.00091688964,0.002298218,0.0015083151,0.0055024256,0.049977493],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003593234,0.000013002603,0.00014601042,0.000071602764,0.000006526109,0.0001538194,0.000079594865,0.000040263727,0.00014286295,0.00070561544,0.9681815,0.030423244],"study_design_scores_gemma":[0.000005084571,0.00002146138,0.00021389671,0.00007205021,0.00000560478,0.0003962365,0.00012417015,0.00006726849,0.00019448884,0.00051597774,0.998376,0.000007728189],"about_ca_topic_score_codex":0.0016656545,"about_ca_topic_score_gemma":0.002076789,"teacher_disagreement_score":0.064051256,"about_ca_system_score_codex":0.0011188415,"about_ca_system_score_gemma":0.0025280565,"threshold_uncertainty_score":0.2142728},"labels":[],"label_agreement":null},{"id":"W3205378782","doi":"10.1080/08982112.2021.1974034","title":"The interdisciplinary nature of network monitoring: Advantages and disadvantages","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","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":"Management science; Data science; Engineering; Computer science","score_opus":0.007941840872076568,"score_gpt":0.3265496635091291,"score_spread":0.31860782263705256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205378782","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024854861,0.050617926,0.23729718,0.59942454,0.005518951,0.0005243128,0.000326492,0.00043957707,0.08099618],"genre_scores_gemma":[0.62076485,0.055473495,0.1857622,0.10906454,0.0140129635,0.0016475002,0.00031107132,0.00056628615,0.0123969745],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.87016857,0.07804688,0.005815985,0.008020405,0.035204705,0.002743436],"domain_scores_gemma":[0.6902232,0.23993571,0.010709947,0.014242183,0.038584188,0.006304693],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.21771908,0.0012415698,0.0014069512,0.003809051,0.00397542,0.014772813,0.0036675138,0.0059992345,0.003979196],"category_scores_gemma":[0.13783345,0.0010118599,0.0017974783,0.005757448,0.008469007,0.024414195,0.014773555,0.010457963,0.000988417],"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":[0.0003421298,0.00009928299,0.023923105,0.002848857,0.00031881063,0.00023239822,0.0054508275,0.0029931744,0.0017416059,0.43933225,0.044935744,0.47778186],"study_design_scores_gemma":[0.00018705698,0.0004900814,0.027166054,0.0073870094,0.00038478116,0.0017727503,0.012954502,0.015638713,0.002893026,0.47989962,0.45088404,0.00034225872],"about_ca_topic_score_codex":0.0026633623,"about_ca_topic_score_gemma":0.0051606833,"teacher_disagreement_score":0.21771908,"about_ca_system_score_codex":0.0056958767,"about_ca_system_score_gemma":0.0083521325,"threshold_uncertainty_score":0.96469164},"labels":[],"label_agreement":null},{"id":"W3205640022","doi":"10.1080/08982112.2021.1974033","title":"Foundations of network monitoring: Definitions and applications","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ambiguity; CLARITY; Field (mathematics); Data science; Computer science; Network monitoring; Point (geometry); Management science; Risk analysis (engineering); Engineering; Business; Mathematics","score_opus":0.048600685322355654,"score_gpt":0.31331340448779155,"score_spread":0.2647127191654359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205640022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00501665,0.035485405,0.8529949,0.038579073,0.0009971117,0.00021633343,0.00031964097,0.00022131077,0.06616963],"genre_scores_gemma":[0.46317184,0.072235055,0.4409283,0.0073695574,0.006771592,0.0013852628,0.0005448061,0.0002499674,0.007343659],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98363,0.008752996,0.001200806,0.002473061,0.003413304,0.0005298495],"domain_scores_gemma":[0.9581434,0.031356487,0.0028188196,0.0030738798,0.00376747,0.00083991926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022554427,0.0014455203,0.0013143872,0.0068788016,0.003308857,0.011339774,0.0035437308,0.005294921,0.0029090273],"category_scores_gemma":[0.031013379,0.0010941311,0.0012348343,0.008566014,0.021558419,0.018532451,0.0066236737,0.009613201,0.000577704],"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":[0.0000059391873,0.000010856621,0.00028851916,0.000099489574,0.0000069012244,0.00002667207,0.00027112322,0.0017455011,0.000058455844,0.98544264,0.0013279635,0.010716063],"study_design_scores_gemma":[0.000005411456,0.000012843025,0.0003481642,0.00047371042,0.000007847102,0.00008334897,0.0002350864,0.0095327655,0.0001434922,0.94954705,0.039586745,0.00002357135],"about_ca_topic_score_codex":0.004266283,"about_ca_topic_score_gemma":0.0017278345,"teacher_disagreement_score":0.022554427,"about_ca_system_score_codex":0.0071249763,"about_ca_system_score_gemma":0.0048032748,"threshold_uncertainty_score":0.11928064},"labels":[],"label_agreement":null},{"id":"W3206556971","doi":"10.1080/08982112.2021.1974036","title":"Broader impacts of network monitoring: Its role in government, industry, technology, and beyond","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Government (linguistics); Business; Engineering; Risk analysis (engineering); Computer security; Engineering management; Computer science","score_opus":0.01294060369167549,"score_gpt":0.2792957351660798,"score_spread":0.2663551314744043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206556971","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005177078,0.05065794,0.010087878,0.88749003,0.008449626,0.00010403978,0.00009055301,0.000065617,0.037877288],"genre_scores_gemma":[0.33326125,0.19614962,0.023120334,0.39837098,0.025785599,0.00042448464,0.00015627955,0.00031459547,0.022416787],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.94074595,0.035260983,0.0023584971,0.0044651637,0.013763002,0.0034064213],"domain_scores_gemma":[0.846684,0.113897376,0.0041685593,0.0056170914,0.022485783,0.0071472824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10817224,0.0012148463,0.0015295147,0.0024408058,0.0070154457,0.029306628,0.0024982782,0.013677412,0.00659231],"category_scores_gemma":[0.059935506,0.0007714409,0.0013719113,0.0028558674,0.021196472,0.029993482,0.014505352,0.024074916,0.0007215841],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013955291,0.00013532524,0.0054816245,0.0015782344,0.000111562826,0.00049618573,0.009853031,0.0028730906,0.0027130407,0.5676616,0.12785086,0.2811059],"study_design_scores_gemma":[0.00001662464,0.0002084886,0.0059841205,0.0048534563,0.00006221676,0.0003087265,0.0101184,0.001119031,0.0008118934,0.14529246,0.8310333,0.00019129836],"about_ca_topic_score_codex":0.0075811287,"about_ca_topic_score_gemma":0.011697733,"teacher_disagreement_score":0.10817224,"about_ca_system_score_codex":0.010552301,"about_ca_system_score_gemma":0.019493913,"threshold_uncertainty_score":0.5720763},"labels":[],"label_agreement":null},{"id":"W3206575254","doi":"10.1080/08982112.2021.1974032","title":"The past, present, and future of network monitoring: A panel discussion","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","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":"Data science; Complex network; Computer science; Network science; Management science; Operations research; Engineering; World Wide Web","score_opus":0.022551228069627168,"score_gpt":0.2852210527427808,"score_spread":0.26266982467315364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206575254","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010351905,0.029077329,0.0013452569,0.95648587,0.004752698,0.000016110784,0.00012973972,0.000014270165,0.007143601],"genre_scores_gemma":[0.16868289,0.18843642,0.006944815,0.56437916,0.05470739,0.00027802424,0.0004696147,0.00012362986,0.015978027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9886135,0.005258696,0.00045091167,0.0022279986,0.0025539626,0.0008949175],"domain_scores_gemma":[0.9554307,0.031577915,0.0018639498,0.001107924,0.0070063365,0.0030132097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05306117,0.0007940733,0.00093546964,0.0018852988,0.005035177,0.017271478,0.0030055458,0.02310656,0.007388627],"category_scores_gemma":[0.036756705,0.00053717423,0.0011089816,0.0035131406,0.005840861,0.031241195,0.0051266225,0.022534268,0.0012933938],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012943783,0.000078756064,0.003600237,0.0008424238,0.00009295516,0.00022660462,0.002593214,0.0020698826,0.0005234588,0.3602808,0.5109131,0.11864909],"study_design_scores_gemma":[0.00003983503,0.00006183896,0.0043847,0.0031694612,0.000095066425,0.00012533076,0.004011426,0.0018974367,0.00037425422,0.12232732,0.86339843,0.00011494079],"about_ca_topic_score_codex":0.008185858,"about_ca_topic_score_gemma":0.011414992,"teacher_disagreement_score":0.05306117,"about_ca_system_score_codex":0.006772878,"about_ca_system_score_gemma":0.009247841,"threshold_uncertainty_score":0.28061765},"labels":[],"label_agreement":null},{"id":"W3207951730","doi":"10.1080/08982112.2021.1974035","title":"Research in network monitoring: Connections with SPM and new directions","year":2021,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","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 Waterloo","funders":"","keywords":"Framing (construction); Data science; Dissemination; Computer science; Management science; Process (computing); Operations research; Engineering; Telecommunications","score_opus":0.08839421435576572,"score_gpt":0.37634759608405555,"score_spread":0.2879533817282898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207951730","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029603078,0.18949936,0.07287043,0.70408636,0.005646361,0.00006150462,0.00014624122,0.00016956725,0.024559913],"genre_scores_gemma":[0.19012019,0.51257867,0.13140947,0.088131405,0.064909905,0.0005429187,0.00029265415,0.0004750719,0.0115397405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96353036,0.024496088,0.0013143014,0.0038675766,0.005867029,0.00092475826],"domain_scores_gemma":[0.74545616,0.21576127,0.005472692,0.014740583,0.014613472,0.0039557638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07750593,0.0014383037,0.0027546973,0.005871826,0.0028707245,0.015825683,0.0037807045,0.011305249,0.008847506],"category_scores_gemma":[0.08453677,0.0010333719,0.0013910892,0.011485428,0.032652132,0.060874745,0.008582795,0.018431691,0.0014714306],"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":[0.00006788255,0.000087465945,0.00202187,0.0010496009,0.00004577788,0.000072211136,0.0012838339,0.0014616776,0.00020734791,0.8296707,0.029197453,0.13483429],"study_design_scores_gemma":[0.000021349668,0.00008356851,0.00085828913,0.001855606,0.000017055969,0.0001458455,0.0014923515,0.0045035365,0.00021875094,0.8431438,0.14760092,0.00005896069],"about_ca_topic_score_codex":0.002757667,"about_ca_topic_score_gemma":0.0027098248,"teacher_disagreement_score":0.07750593,"about_ca_system_score_codex":0.006321103,"about_ca_system_score_gemma":0.007451038,"threshold_uncertainty_score":0.40989542},"labels":[],"label_agreement":null},{"id":"W4211141605","doi":"10.1080/08982110701777486","title":"Rejoinder","year":2007,"lang":"en","type":"article","venue":"Quality Engineering","topic":"","field":"","cited_by":1,"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 Waterloo","funders":"","keywords":"Business","score_opus":0.040022937116578026,"score_gpt":0.33577011406486273,"score_spread":0.29574717694828473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211141605","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00190241,0.0031056819,0.0029279736,0.7275103,0.14180022,0.000098348035,0.00023512192,0.00042932003,0.12199075],"genre_scores_gemma":[0.012623792,0.00085989374,0.00120731,0.5985743,0.019892378,0.000100399884,0.00013102707,0.00030163527,0.36630931],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9874149,0.0015212799,0.00048626572,0.0024253272,0.0063602715,0.0017918964],"domain_scores_gemma":[0.98447824,0.00424048,0.00042646684,0.0020770256,0.0070386874,0.0017390383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007887666,0.0009591471,0.0013886039,0.0017045276,0.009379626,0.012984146,0.0046571633,0.03953163,0.03647002],"category_scores_gemma":[0.03751486,0.00057648285,0.0018659445,0.0009348517,0.0058353613,0.0069782473,0.0068441085,0.037577298,0.01930639],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025318966,0.000019269537,0.000101251535,0.00003666091,0.000009894326,0.00014352669,0.00033726846,0.000037702095,0.00018379276,0.05269717,0.9387611,0.007646983],"study_design_scores_gemma":[0.000014411518,0.000012756407,0.00022390489,0.000088220564,0.000012141734,0.00005551625,0.00034376557,0.00007271728,0.00014082262,0.011743817,0.9872749,0.000017116778],"about_ca_topic_score_codex":0.016963087,"about_ca_topic_score_gemma":0.030412618,"teacher_disagreement_score":0.03953163,"about_ca_system_score_codex":0.0043550916,"about_ca_system_score_gemma":0.0077914735,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4254031646","doi":"10.1080/08982112.2019.1599946","title":"Rejoinder","year":2019,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Business; Computer science","score_opus":0.17744025718834733,"score_gpt":0.46549794508436726,"score_spread":0.28805768789601993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254031646","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035520032,0.0021211486,0.0013870395,0.8930235,0.08536605,0.000018454255,0.00008793443,0.000079264064,0.017561436],"genre_scores_gemma":[0.0065703373,0.00071340404,0.0011494859,0.905626,0.041003507,0.000088167304,0.00005518921,0.00014481647,0.044649158],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9834049,0.0037147424,0.000765696,0.0033004314,0.007514362,0.0012998204],"domain_scores_gemma":[0.95837134,0.019291796,0.001416353,0.004361464,0.013231469,0.0033275972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014929424,0.00078944984,0.0018263629,0.0017512367,0.0079070525,0.011301168,0.005224962,0.036222532,0.018400485],"category_scores_gemma":[0.08551421,0.0005053684,0.0013641911,0.0011870888,0.013152026,0.009419861,0.008486611,0.04974141,0.011993962],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017130786,0.000011217109,0.000085163025,0.000035817226,0.000011052938,0.00007063469,0.00036880941,0.000032736756,0.000045563665,0.08334314,0.91136956,0.004609106],"study_design_scores_gemma":[0.000022503027,0.000009123053,0.00017344052,0.0002111694,0.000013147574,0.000072957875,0.0005509989,0.00008694181,0.00008041064,0.082326055,0.9164288,0.000024407129],"about_ca_topic_score_codex":0.006988997,"about_ca_topic_score_gemma":0.011297567,"teacher_disagreement_score":0.036222532,"about_ca_system_score_codex":0.0039263316,"about_ca_system_score_gemma":0.00637547,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4292227971","doi":"10.1080/08982112.2022.2106440","title":"Statistical engineering – Part 2: Future","year":2022,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Leverage (statistics); Government (linguistics); Order (exchange); Computer science; Key (lock); Data science; Management science; Operations research; Engineering ethics; Engineering; Economics; Artificial intelligence; Computer security","score_opus":0.11023798342743484,"score_gpt":0.4173955416998471,"score_spread":0.30715755827241226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292227971","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027936138,0.24780774,0.109372646,0.52489936,0.03364383,0.00013353986,0.00021329253,0.0002917882,0.080844134],"genre_scores_gemma":[0.12389767,0.3884755,0.09728117,0.20370145,0.088417776,0.0008952437,0.00059612887,0.0007838713,0.09595117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9847869,0.0085018575,0.00071242056,0.0017684135,0.0036926686,0.00053760683],"domain_scores_gemma":[0.95324373,0.031198831,0.0013032955,0.003927981,0.008760512,0.0015656715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040858794,0.0012236731,0.0009879288,0.0024739217,0.0021959373,0.008513907,0.001867333,0.008408814,0.011738902],"category_scores_gemma":[0.03832243,0.00073082926,0.0013199741,0.0026176476,0.010844601,0.015530314,0.004030567,0.010723527,0.004836421],"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":[0.000059939837,0.00011139715,0.0007173253,0.0006112061,0.000034345754,0.00011955934,0.00060605333,0.0019803043,0.0005034803,0.6497653,0.13851386,0.20697711],"study_design_scores_gemma":[0.000011180636,0.00012280505,0.0006789514,0.0011025199,0.000010347935,0.00026780608,0.00042586128,0.0020159667,0.00038345976,0.397328,0.59760356,0.000049496313],"about_ca_topic_score_codex":0.0015073647,"about_ca_topic_score_gemma":0.0011001481,"teacher_disagreement_score":0.040858794,"about_ca_system_score_codex":0.0046983254,"about_ca_system_score_gemma":0.0056939814,"threshold_uncertainty_score":0.21608454},"labels":[],"label_agreement":null},{"id":"W4386599896","doi":"10.1080/08982112.2023.2253303","title":"Verifying a dominant cause of output variation","year":2023,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Universiteit van Amsterdam","keywords":"Variation (astronomy); Econometrics; Computer science; Statistics; Economics; Mathematics; Physics","score_opus":0.22707937703777803,"score_gpt":0.4270334173580642,"score_spread":0.19995404032028619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386599896","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1802393,0.0011731007,0.7984651,0.0028034425,0.00072423596,0.0029208122,0.00092341803,0.0005652132,0.012185382],"genre_scores_gemma":[0.77390516,0.00037358006,0.22132927,0.0007932499,0.00016348186,0.0017610426,0.00026568747,0.00008270908,0.0013258414],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8594159,0.08102574,0.009598943,0.021394765,0.026589293,0.0019754057],"domain_scores_gemma":[0.42794377,0.46886185,0.034065332,0.04570119,0.022126105,0.0013017319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14329965,0.0011260208,0.0020545267,0.0024134114,0.0021260534,0.0033399519,0.0022289897,0.0027688937,0.009183496],"category_scores_gemma":[0.32073674,0.00067417923,0.0031875793,0.0016859962,0.006415744,0.0037670706,0.003225995,0.0028937538,0.00075054786],"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":[0.0072402135,0.002533353,0.1513936,0.0070719607,0.0037972855,0.0012129713,0.006539747,0.025222354,0.034642514,0.37153018,0.0061233314,0.3826926],"study_design_scores_gemma":[0.0023105848,0.012179683,0.124901116,0.0026360995,0.00397845,0.0010720493,0.0064908545,0.12736277,0.102421775,0.5812171,0.034852773,0.00057675247],"about_ca_topic_score_codex":0.0023581188,"about_ca_topic_score_gemma":0.0021762983,"teacher_disagreement_score":0.14329965,"about_ca_system_score_codex":0.0029381127,"about_ca_system_score_gemma":0.008674317,"threshold_uncertainty_score":0.75785005},"labels":[],"label_agreement":null},{"id":"W4389551093","doi":"10.1080/08982112.2023.2286500","title":"Utilizing jackknife and bootstrap to understand tensile stress to failure of an epoxy resin","year":2023,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Jackknife resampling; Weibull distribution; Ultimate tensile strength; Percentile; Statistics; Reliability (semiconductor); Point estimation; Resampling; Mathematics; Weibull modulus; Stress (linguistics); Epoxy; Sample size determination; Replicate; Materials science; Composite material; Power (physics)","score_opus":0.2630767744072768,"score_gpt":0.41744897524886226,"score_spread":0.15437220084158548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389551093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13579918,0.00033721337,0.8616349,0.00008056481,0.000029445266,0.00012595674,0.0000960141,0.00034588078,0.0015507452],"genre_scores_gemma":[0.7344701,0.00025869496,0.26414648,0.00005848026,0.000019554798,0.0002475838,0.00023306841,0.00009044515,0.00047563185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958994,0.0017755203,0.00023272507,0.0005289945,0.0014090196,0.00015447532],"domain_scores_gemma":[0.9694268,0.022129465,0.0025728168,0.0023796347,0.0032967671,0.00019460157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011039081,0.00068712945,0.00083309895,0.0025286903,0.0004844238,0.0006368346,0.00095515116,0.00071159477,0.00091917475],"category_scores_gemma":[0.04992868,0.000290639,0.0006316746,0.0011244462,0.00076252496,0.0014071495,0.0006490252,0.00083655014,0.0002552672],"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":[0.00083477603,0.00031053118,0.06698646,0.00079029356,0.00042357013,0.00085333106,0.0021240148,0.48555896,0.024104891,0.033139266,0.0017464398,0.3831275],"study_design_scores_gemma":[0.000031430576,0.0006626652,0.02633354,0.00011403235,0.000069783004,0.00049305335,0.000608416,0.9271147,0.015121485,0.026019376,0.0033324664,0.000099006254],"about_ca_topic_score_codex":0.002804688,"about_ca_topic_score_gemma":0.0027190824,"teacher_disagreement_score":0.011039081,"about_ca_system_score_codex":0.0006185616,"about_ca_system_score_gemma":0.00094885135,"threshold_uncertainty_score":0.0583809},"labels":[],"label_agreement":null},{"id":"W4400654206","doi":"10.1080/08982112.2024.2375612","title":"A study of attribute control charts for censored lifetime data","year":2024,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control chart; Statistical process control; Statistics; Control (management); Computer science; Econometrics; Business; Mathematics; Artificial intelligence; Process (computing)","score_opus":0.29844668607346997,"score_gpt":0.5094505218667869,"score_spread":0.21100383579331689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400654206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1192389,0.0039398535,0.8733099,0.00016812146,0.00014931487,0.00018681599,0.00018668365,0.00071149925,0.0021089532],"genre_scores_gemma":[0.8620385,0.0013054487,0.13518496,0.00007298728,0.00015908231,0.00012285217,0.00035789836,0.000107545464,0.0006507243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9859038,0.007410917,0.00048200416,0.0016488285,0.004223632,0.00033076477],"domain_scores_gemma":[0.79805946,0.17528231,0.007046386,0.007481824,0.011134711,0.0009953054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02434927,0.0009345285,0.0014433668,0.0028108268,0.0005966694,0.0023543066,0.0011065365,0.00083726464,0.00077709527],"category_scores_gemma":[0.11805286,0.000314041,0.00089454226,0.0030233688,0.0016401847,0.002671547,0.0006653525,0.0019096197,0.00012000412],"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":[0.0016815205,0.00044225503,0.024520954,0.0007065733,0.00055069773,0.00025510028,0.000654613,0.5252393,0.00713967,0.10586009,0.0015734973,0.33137572],"study_design_scores_gemma":[0.00004298721,0.0008484869,0.005403482,0.00009120985,0.000102031394,0.00013765461,0.000077664634,0.96892965,0.00802969,0.014365432,0.0018840731,0.00008755994],"about_ca_topic_score_codex":0.0037656927,"about_ca_topic_score_gemma":0.0009253677,"teacher_disagreement_score":0.02434927,"about_ca_system_score_codex":0.0012335846,"about_ca_system_score_gemma":0.001419545,"threshold_uncertainty_score":0.1287728},"labels":[],"label_agreement":null},{"id":"W4402438503","doi":"10.1080/08982112.2024.2381005","title":"On the construction of saturated split-plot designs for quadratic response surface models","year":2024,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","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 Manitoba","funders":"","keywords":"Response surface methodology; Plot (graphics); Mathematics; Quadratic equation; Surface (topology); Statistics; Econometrics; Geometry","score_opus":0.3980598917181631,"score_gpt":0.4867958580602684,"score_spread":0.08873596634210534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402438503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026589609,0.00006287503,0.9967283,0.00002452961,0.0000055765754,0.00008128668,0.00003095704,0.00010741379,0.0002999717],"genre_scores_gemma":[0.14350861,0.00025731119,0.8535885,0.00013008383,0.000023028375,0.0012360816,0.00026446048,0.0001246109,0.0008673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9889544,0.008313618,0.0003655366,0.00096847204,0.0011666601,0.00023125691],"domain_scores_gemma":[0.96789294,0.026375385,0.001670613,0.001571296,0.002226457,0.00026332898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014953758,0.0015216259,0.0014204957,0.0013497312,0.00036615226,0.00093949575,0.0011760886,0.0012256369,0.0050088246],"category_scores_gemma":[0.037652913,0.0010582014,0.0016136949,0.0010362575,0.0014276862,0.0014634514,0.0020507055,0.001647955,0.0011376104],"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":[0.0011098748,0.00030373526,0.0027227497,0.0011341216,0.000223409,0.00017548565,0.00038028567,0.6613807,0.020717865,0.105152056,0.0015914424,0.20510827],"study_design_scores_gemma":[0.00010658017,0.0008149084,0.0007188288,0.00008240143,0.000041091334,0.000054648674,0.000030434383,0.9460058,0.004143018,0.045438934,0.0025290088,0.000034360677],"about_ca_topic_score_codex":0.0007658395,"about_ca_topic_score_gemma":0.0008349491,"teacher_disagreement_score":0.014953758,"about_ca_system_score_codex":0.0007187839,"about_ca_system_score_gemma":0.0014995708,"threshold_uncertainty_score":0.07908398},"labels":[],"label_agreement":null},{"id":"W4403059377","doi":"10.1080/08982112.2024.2410012","title":"A review of leveraged sample selection in variation reduction projects","year":2024,"lang":"en","type":"review","venue":"Quality Engineering","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":"Selection (genetic algorithm); Sample (material); Variation (astronomy); Reduction (mathematics); Business; Engineering; Statistics; Operations management; Manufacturing engineering; Computer science; Mathematics; Artificial intelligence; Chemistry","score_opus":0.07500702337839112,"score_gpt":0.3798421957638481,"score_spread":0.304835172385457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403059377","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041344087,0.98776746,0.0075506493,0.00068924,0.00020685552,0.00007140287,0.00009972705,0.000040973708,0.003160379],"genre_scores_gemma":[0.005393578,0.9845664,0.008027872,0.0004317244,0.00035564465,0.00012310107,0.00015960129,0.000027754098,0.000914267],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99560106,0.0017418609,0.00056561735,0.0005995396,0.001358699,0.0001331781],"domain_scores_gemma":[0.9728969,0.021870296,0.001519991,0.000618721,0.0028016376,0.0002924155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008881238,0.0014231062,0.0021640891,0.0055389376,0.0005812434,0.0024395254,0.002351551,0.0019255384,0.0064165615],"category_scores_gemma":[0.024578419,0.000951687,0.0018438648,0.009612961,0.0010414087,0.0025690554,0.0012254642,0.0016351847,0.0024469912],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083871615,0.00010079222,0.0012084811,0.034260713,0.0003288244,0.00012759364,0.00015845399,0.0030446385,0.0004563255,0.014380552,0.01463968,0.93121004],"study_design_scores_gemma":[0.000055051012,0.0005003642,0.004293058,0.033486083,0.00093522173,0.00089772715,0.00023208463,0.002842843,0.0013630976,0.015091806,0.9401854,0.000117223615],"about_ca_topic_score_codex":0.0039432,"about_ca_topic_score_gemma":0.0050574807,"teacher_disagreement_score":0.008881238,"about_ca_system_score_codex":0.0014672735,"about_ca_system_score_gemma":0.0041242954,"threshold_uncertainty_score":0.046969056},"labels":[],"label_agreement":null},{"id":"W4405189958","doi":"10.1080/08982112.2024.2434019","title":"A Conversation with Christine M. Anderson-Cook","year":2024,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","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":"Conversation; Management; Engineering; Marketing; Sociology; Operations research; Advertising; Business; Economics; Communication","score_opus":0.21817104021291212,"score_gpt":0.5007267483288038,"score_spread":0.2825557081158917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405189958","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018468328,0.02115487,0.0006360184,0.94136894,0.018760022,0.000024336448,0.00006398551,0.000042919757,0.016102187],"genre_scores_gemma":[0.04508051,0.02393071,0.00213436,0.84580415,0.008318255,0.000136323,0.00007795508,0.00019181422,0.07432588],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99598205,0.0019914338,0.00013099803,0.00057247555,0.00089595764,0.00042710252],"domain_scores_gemma":[0.9836774,0.008046834,0.00045371352,0.0002404636,0.0030629123,0.004518737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008075185,0.0006898551,0.0009393597,0.001065995,0.009979508,0.005670763,0.0015691208,0.006610666,0.013911443],"category_scores_gemma":[0.028679714,0.0005843291,0.00046485424,0.0011102638,0.004149976,0.0061690533,0.0032165179,0.017627569,0.0033469207],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000146995935,0.000025259797,0.00023047492,0.00005147944,0.0000057827774,0.0002187337,0.0049053165,0.000028591126,0.00007419044,0.0052975323,0.9824112,0.006736768],"study_design_scores_gemma":[0.0000073492106,0.000017510569,0.00039249187,0.0003230902,0.0000043202217,0.00043753366,0.010347009,0.000055134435,0.0000839289,0.0014614069,0.9868385,0.00003171901],"about_ca_topic_score_codex":0.040266544,"about_ca_topic_score_gemma":0.07882243,"teacher_disagreement_score":0.040266544,"about_ca_system_score_codex":0.0072440715,"about_ca_system_score_gemma":0.009134939,"threshold_uncertainty_score":0.08006436},"labels":[],"label_agreement":null},{"id":"W4414223511","doi":"10.1080/08982112.2025.2550316","title":"Assessing counting systems using a gold standard or a true count","year":2025,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gold standard (test); Standard deviation; Range (aeronautics); Statistical analysis","score_opus":0.05838528460772626,"score_gpt":0.39536396065040297,"score_spread":0.33697867604267673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414223511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016198402,0.0005941595,0.9720233,0.0037865224,0.00016626857,0.00024607233,0.00032626657,0.0005385319,0.006120356],"genre_scores_gemma":[0.43926287,0.0004974623,0.55440426,0.0010512458,0.00022919291,0.0007713008,0.0005051758,0.00012903036,0.0031494424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9158142,0.03410755,0.00517305,0.0151604,0.02780554,0.001939225],"domain_scores_gemma":[0.84737265,0.091739476,0.020192098,0.014486269,0.024259752,0.0019497172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.060377263,0.0022013497,0.0027317968,0.008645179,0.0018013418,0.011952023,0.0062354766,0.0052077,0.0028744806],"category_scores_gemma":[0.1693112,0.0010039404,0.002065028,0.003918442,0.0076272427,0.012671492,0.0058686454,0.003847075,0.0014964767],"study_design_candidate":"bench_or_experimental","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":[0.00046060755,0.0005093462,0.09531145,0.0011804148,0.00078624213,0.00048665883,0.0031779313,0.11175184,0.0042279437,0.4988596,0.011061196,0.2721868],"study_design_scores_gemma":[0.000096883785,0.00090740673,0.01968106,0.0006299663,0.0002723173,0.0007286338,0.0012431924,0.5103422,0.0057673804,0.44565642,0.014272742,0.00040191427],"about_ca_topic_score_codex":0.006923731,"about_ca_topic_score_gemma":0.0061141956,"teacher_disagreement_score":0.060377263,"about_ca_system_score_codex":0.0059112147,"about_ca_system_score_gemma":0.005437039,"threshold_uncertainty_score":0.31930923},"labels":[],"label_agreement":null},{"id":"W4416881945","doi":"10.1080/08982112.2025.2592021","title":"Question, plan, data, Analysis, and Conclusion (QPDAC): A tutorial","year":2025,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Academic Writing and Publishing","field":"Arts and Humanities","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"","score_opus":0.04119568836457294,"score_gpt":0.30840288626687223,"score_spread":0.2672071979022993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416881945","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010313381,0.017177723,0.91231453,0.029304855,0.005213492,0.008648297,0.0010558459,0.0060974252,0.019156512],"genre_scores_gemma":[0.0041634487,0.011265126,0.95201385,0.0074339802,0.0020882234,0.007659325,0.0011799892,0.0017284463,0.0124676395],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.94489175,0.036813248,0.006449263,0.001923336,0.00903756,0.0008848354],"domain_scores_gemma":[0.8296369,0.119813755,0.009932208,0.007366342,0.02904341,0.0042074188],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07327187,0.0035417578,0.0023073712,0.008014708,0.0029631022,0.009792602,0.0044835466,0.004571544,0.027757348],"category_scores_gemma":[0.14389667,0.0023317223,0.0031322944,0.0056816735,0.0056446088,0.012655853,0.0070491843,0.010378838,0.032980733],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011755507,0.00026442314,0.00060733507,0.006379922,0.00009521224,0.0002470315,0.005107986,0.002138052,0.0032918507,0.068698704,0.21717575,0.69587606],"study_design_scores_gemma":[0.00003804175,0.0001447718,0.0005383236,0.0058473772,0.00003242892,0.00058752584,0.0011269411,0.0029647383,0.0017032977,0.04372874,0.94316566,0.00012209507],"about_ca_topic_score_codex":0.0033350014,"about_ca_topic_score_gemma":0.0043189363,"teacher_disagreement_score":0.9267281,"about_ca_system_score_codex":0.0059643527,"about_ca_system_score_gemma":0.01821137,"threshold_uncertainty_score":0.38750333},"labels":[],"label_agreement":null},{"id":"W603141552","doi":"","title":"Application of quality control in ICR data capture 2001 Canadian census of agriculture","year":2005,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Quality assurance; Statistical process control; Quality (philosophy); Control (management); Data quality; Computer science; Process (computing); Automatic identification and data capture; Database; Operations research; Data science; Engineering; Operations management; Artificial intelligence","score_opus":0.018614228288967593,"score_gpt":0.27196581602623005,"score_spread":0.25335158773726246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W603141552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26698822,0.006560693,0.4374038,0.010019559,0.00087448215,0.021296198,0.13689585,0.0025543883,0.11740684],"genre_scores_gemma":[0.5960258,0.00304004,0.34847483,0.0011735201,0.00011230248,0.004031497,0.03657281,0.00029412337,0.010275093],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.94487566,0.015060497,0.0036546018,0.0033859194,0.031545833,0.0014774405],"domain_scores_gemma":[0.9160828,0.009434467,0.0066716447,0.0047436943,0.062332485,0.0007348614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024250323,0.00049826864,0.0005130899,0.0069876406,0.0019059044,0.002236048,0.0018927454,0.0005164554,0.001398009],"category_scores_gemma":[0.08205373,0.00043322434,0.0004242589,0.014479942,0.0010733018,0.0005712765,0.0012839596,0.0007828242,0.0004205658],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031440138,0.00014061744,0.38402387,0.0013232835,0.00029206745,0.00018861284,0.0032806618,0.020943804,0.0042322683,0.014412793,0.059275582,0.51157206],"study_design_scores_gemma":[0.0000925206,0.00022418435,0.8420058,0.00046054245,0.0001407775,0.00012430029,0.0017169323,0.03385974,0.010629722,0.0016817828,0.10887742,0.00018633506],"about_ca_topic_score_codex":0.94402796,"about_ca_topic_score_gemma":0.9089671,"teacher_disagreement_score":0.05597204,"about_ca_system_score_codex":0.036366772,"about_ca_system_score_gemma":0.03297941,"threshold_uncertainty_score":0.26386064},"labels":[],"label_agreement":null},{"id":"W7104179287","doi":"10.1080/08982112.2025.2581188","title":"Control charting with interval-valued data","year":2025,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","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":"Control chart; Interval (graph theory); Control (management); Process (computing); Statistical process control; Interval data","score_opus":0.2092703810842778,"score_gpt":0.49438299340528175,"score_spread":0.28511261232100393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104179287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017664647,0.00018842208,0.9971775,0.00003293878,0.000019025038,0.000060379505,0.000040109724,0.00043805456,0.0002771364],"genre_scores_gemma":[0.16356035,0.0006622049,0.83440965,0.000055770506,0.000097840246,0.00037484473,0.0002862521,0.00010183735,0.00045126252],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903701,0.0048989807,0.0006332818,0.0010952244,0.0028126617,0.00018971619],"domain_scores_gemma":[0.96078455,0.027825907,0.0041771326,0.0027797944,0.004136939,0.0002955436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010934264,0.0010801032,0.0013167509,0.002605572,0.0004204744,0.0024692323,0.002226407,0.00083472783,0.0015715521],"category_scores_gemma":[0.033759955,0.00046878678,0.0010190659,0.0033039593,0.0019078236,0.00238725,0.00089829793,0.0017467986,0.00023930665],"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":[0.0005782501,0.00019579212,0.0033155275,0.0008904447,0.00027241057,0.00013135557,0.0004816844,0.38778058,0.0051719076,0.10567804,0.0022972,0.4932068],"study_design_scores_gemma":[0.0001545741,0.00029400026,0.00074121694,0.00014628362,0.000058625745,0.00008542899,0.000027394732,0.9576642,0.01061845,0.024319323,0.005773135,0.00011747046],"about_ca_topic_score_codex":0.00331091,"about_ca_topic_score_gemma":0.0013884049,"teacher_disagreement_score":0.010934264,"about_ca_system_score_codex":0.0010154045,"about_ca_system_score_gemma":0.0014131571,"threshold_uncertainty_score":0.05782658},"labels":[],"label_agreement":null}]}