{"meta":{"query_hash":"501ebe49e7f4","filters":{"venue":"Communications in Statistics Case Studies Data Analysis and Applications"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/501ebe49e7f4","api":"https://metacan.xera.ac/api/v1/cohort?venue=Communications+in+Statistics+Case+Studies+Data+Analysis+and+Applications"},"results":[{"id":"W2944250068","doi":"10.1080/23737484.2019.1605632","title":"Destructive cure rate models under proportional odds and associated likelihood inference","year":2019,"lang":"en","type":"article","venue":"Communications in Statistics Case Studies Data Analysis and Applications","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Negative binomial distribution; Weibull distribution; Statistics; Poisson distribution; Odds; Mathematics; Inference; Poisson regression; Econometrics; Expectation–maximization algorithm; Logistic regression; Maximization; Maximum likelihood; Computer science; Artificial intelligence; Mathematical optimization; Medicine; Population","score_opus":0.22524181492870243,"score_gpt":0.46902441878724566,"score_spread":0.24378260385854322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944250068","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":[0.007161497,0.00028037667,0.9908489,0.00024733014,0.00002554835,0.00009361184,0.00017814215,0.00013614584,0.0010284411],"genre_scores_gemma":[0.5084841,0.0018237772,0.47360098,0.0005732204,0.00043397007,0.0016568593,0.0016293918,0.00025659014,0.011541019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98961043,0.005896162,0.0004679488,0.0015321479,0.001957821,0.0005354593],"domain_scores_gemma":[0.9585227,0.03433923,0.002843793,0.0022590011,0.0017121432,0.0003230874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023147894,0.0017234784,0.002402119,0.002727315,0.00082088355,0.0028629887,0.006713227,0.002209174,0.0038394858],"category_scores_gemma":[0.05915856,0.0011616844,0.0026443403,0.0025103039,0.0029347995,0.0041147294,0.0035888557,0.0043926723,0.0009505656],"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":[0.00014429443,0.00011386199,0.005557166,0.0002595563,0.00018689522,0.00056538294,0.0004491988,0.49663565,0.0006037279,0.44436517,0.0014347874,0.049684368],"study_design_scores_gemma":[0.000021414558,0.00003488871,0.00038846766,0.000022605924,0.000034277196,0.00014252505,0.000038102866,0.8716655,0.00024927646,0.12642142,0.00095736724,0.000024220326],"about_ca_topic_score_codex":0.0032748217,"about_ca_topic_score_gemma":0.001974852,"teacher_disagreement_score":0.023147894,"about_ca_system_score_codex":0.0016004461,"about_ca_system_score_gemma":0.0018252748,"threshold_uncertainty_score":0.12241924},"labels":[],"label_agreement":null},{"id":"W3113334358","doi":"10.1080/23737484.2020.1850372","title":"A wavelet-based approach for Johansen’s likelihood ratio test for cointegration in the presence of measurement errors: An application to CO<sub>2</sub> emissions and real GDP data","year":2020,"lang":"en","type":"article","venue":"Communications in Statistics Case Studies Data Analysis and Applications","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"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":"Cointegration; Econometrics; Wavelet; Statistics; Statistical hypothesis testing; Johansen test; Inference; Monte Carlo method; Sample (material); Economics; Sample size determination; Observational error; Mathematics; Error correction model; Computer science; Artificial intelligence","score_opus":0.31711683345393143,"score_gpt":0.375983602162767,"score_spread":0.05886676870883556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113334358","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":[0.0068957354,0.00017812799,0.9916871,0.000361831,0.00005418616,0.00003097355,0.000044012555,0.000081356324,0.00066663336],"genre_scores_gemma":[0.2318439,0.0007562751,0.7650904,0.00021945,0.00033420636,0.0003311364,0.0002359538,0.00014379754,0.0010448935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9905785,0.0066183126,0.00046662442,0.0007000668,0.0014342358,0.00020220903],"domain_scores_gemma":[0.9664394,0.028517935,0.0014359646,0.0018897185,0.0015433922,0.00017353008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012301632,0.0006467963,0.001049742,0.0028268828,0.00051416695,0.0016348967,0.001384823,0.0015914721,0.0026052028],"category_scores_gemma":[0.07183168,0.000509281,0.0014477217,0.0035464647,0.0016098982,0.0022411284,0.0016288573,0.0026408238,0.0006480585],"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":[0.00019171704,0.00019880396,0.011434908,0.0003949227,0.00038633874,0.0010563228,0.0006239376,0.14004365,0.0064533,0.35837647,0.0046015973,0.47623813],"study_design_scores_gemma":[0.000048518537,0.00014035584,0.0038951288,0.00006369788,0.00007021403,0.00022903403,0.00015267043,0.8482833,0.0025386345,0.13964897,0.0048614275,0.000068016394],"about_ca_topic_score_codex":0.0013669905,"about_ca_topic_score_gemma":0.0010660848,"teacher_disagreement_score":0.012301632,"about_ca_system_score_codex":0.0006393849,"about_ca_system_score_gemma":0.0011423733,"threshold_uncertainty_score":0.06505805},"labels":[],"label_agreement":null},{"id":"W3210385378","doi":"10.1080/23737484.2021.1991855","title":"Early detection of individual growing pigs’ sanitary challenges using functional data analysis of real-time feed intake patterns","year":2021,"lang":"en","type":"article","venue":"Communications in Statistics Case Studies Data Analysis and Applications","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","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":"Agriculture and Agri-Food Canada; Université de Sherbrooke","funders":"Agriculture and Agri-Food Canada; Swine Innovation Porc","keywords":"Herd; Statistics; Animal science; Computer science; Biology; Mathematics","score_opus":0.3326020017364586,"score_gpt":0.4336694862607667,"score_spread":0.10106748452430808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210385378","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":[0.52035743,0.00015385995,0.475718,0.00009061278,0.000028648914,0.00014579564,0.000624475,0.0008119562,0.0020691694],"genre_scores_gemma":[0.89143914,0.00007918285,0.10738256,0.000017811819,0.000016510876,0.000100325866,0.00037216776,0.000022020558,0.00057034194],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996784,0.000092397466,0.000021788228,0.000094869574,0.00008145972,0.000031071828],"domain_scores_gemma":[0.9988619,0.0005243153,0.00023237521,0.0001210623,0.000194244,0.00006612861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006987073,0.0004109892,0.00031466648,0.0014228928,0.00017541861,0.00045677286,0.0003995491,0.00039810015,0.0008609343],"category_scores_gemma":[0.0021236471,0.00012340481,0.0002525486,0.00042434686,0.0002601361,0.00037068632,0.0004017,0.00018798947,0.00026155688],"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":[0.0010525645,0.0004346473,0.26394454,0.000477932,0.00016908653,0.0005588742,0.0009126917,0.041211944,0.20705791,0.0029654156,0.0012239189,0.4799905],"study_design_scores_gemma":[0.000022391752,0.0009932348,0.41266277,0.000084852676,0.000085102445,0.0008477523,0.00060615886,0.5131814,0.064680025,0.0034701554,0.0032595005,0.00010666748],"about_ca_topic_score_codex":0.0012163288,"about_ca_topic_score_gemma":0.0022728657,"teacher_disagreement_score":0.0014228928,"about_ca_system_score_codex":0.00019992403,"about_ca_system_score_gemma":0.0004346299,"threshold_uncertainty_score":0.0036951303},"labels":[],"label_agreement":null},{"id":"W4286685933","doi":"10.1080/23737484.2022.2093294","title":"Evaluation of the forecasting accuracy of stochastic mortality models: An analysis of developed and developing countries","year":2022,"lang":"en","type":"article","venue":"Communications in Statistics Case Studies Data Analysis and Applications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Mortality rate; Demography; Cohort; Age groups; Developing country; Statistics; Geography; Econometrics; Medicine; Mathematics; Economics; Economic growth; Sociology","score_opus":0.40445728650811863,"score_gpt":0.4905363894244721,"score_spread":0.08607910291635346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286685933","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":[0.9848963,0.0011481503,0.009903878,0.00034795402,0.000035015153,0.000030310372,0.0010903105,0.00009042587,0.0024577617],"genre_scores_gemma":[0.99618465,0.00041985014,0.0019927518,0.000019996192,0.000011406295,0.000012450554,0.0012100467,0.000009320624,0.00013948648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998654,0.00066185155,0.00014262908,0.00018302779,0.00022226958,0.00013621773],"domain_scores_gemma":[0.9919693,0.004574245,0.0010522628,0.00084519124,0.0013547842,0.00020430994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006893159,0.0006186447,0.0007503112,0.0022606247,0.00033779454,0.0010173226,0.0006321923,0.00056777115,0.00034069314],"category_scores_gemma":[0.0149399815,0.00021728966,0.0009975638,0.0023286496,0.00043553006,0.0008961184,0.00083929725,0.00069412385,0.00009995976],"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":[0.00024080623,0.00008671223,0.23780948,0.00009687739,0.000442834,0.0003441405,0.00017711948,0.7361096,0.00042711792,0.0027155576,0.0009753303,0.020574493],"study_design_scores_gemma":[0.000034829372,0.00027857826,0.10885409,0.000111611625,0.00016498324,0.00019962706,0.000588801,0.88392174,0.0014867565,0.002438293,0.0018708765,0.000049798033],"about_ca_topic_score_codex":0.02369091,"about_ca_topic_score_gemma":0.007427891,"teacher_disagreement_score":0.02369091,"about_ca_system_score_codex":0.00088430423,"about_ca_system_score_gemma":0.0008025767,"threshold_uncertainty_score":0.047106028},"labels":[],"label_agreement":null},{"id":"W4405003222","doi":"10.1080/23737484.2024.2429113","title":"The odyssey of women in university education: stochastic growth models for studying access and graduation process","year":2024,"lang":"en","type":"article","venue":"Communications in Statistics Case Studies Data Analysis and Applications","topic":"School Choice and Performance","field":"Social 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":"Interpretability; Bachelor; Graduation (instrument); Process (computing); Interpretation (philosophy); Computer science; Event (particle physics); Econometrics; Data science; Empirical research; Regression analysis; Management science; Operations research; Statistics; Artificial intelligence; Machine learning; Mathematics; Political science; Economics","score_opus":0.1858231275887416,"score_gpt":0.4709202140950286,"score_spread":0.285097086506287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405003222","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":[0.39358103,0.004849626,0.5749465,0.009720879,0.00024199592,0.00022279087,0.0018222955,0.0002604021,0.014354472],"genre_scores_gemma":[0.96216315,0.0030014822,0.022740746,0.0002908813,0.00019444345,0.0002723801,0.00069121574,0.00007067196,0.010575096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845064,0.000924649,0.00005260987,0.00027955696,0.00012944416,0.00016304098],"domain_scores_gemma":[0.9881881,0.008956737,0.0014855204,0.0004186055,0.0003828244,0.0005683113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064488994,0.00087777566,0.0011656381,0.0023016427,0.000720984,0.002367814,0.0016780165,0.0020294238,0.004878627],"category_scores_gemma":[0.02136161,0.00053375086,0.0014074175,0.002316599,0.0023519266,0.0029595937,0.0021500906,0.0026670939,0.00075445743],"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":[0.00014275033,0.00015601215,0.051332776,0.00019477606,0.00019127189,0.00047649108,0.0019944108,0.42088678,0.00051156216,0.5025347,0.0034274817,0.018150946],"study_design_scores_gemma":[0.000016535116,0.00006850361,0.005946531,0.00005940366,0.00003491106,0.00009515937,0.0006158105,0.84371537,0.00009541536,0.14579237,0.0035178931,0.00004212696],"about_ca_topic_score_codex":0.02316592,"about_ca_topic_score_gemma":0.015188806,"teacher_disagreement_score":0.02316592,"about_ca_system_score_codex":0.0020693452,"about_ca_system_score_gemma":0.0014396487,"threshold_uncertainty_score":0.04606217},"labels":[],"label_agreement":null}]}