{"meta":{"query_hash":"55705db836a2","filters":{"venue":"Journal of Statistical and Econometric Methods"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/55705db836a2","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Statistical+and+Econometric+Methods"},"results":[{"id":"W2187335251","doi":"","title":"Comparing the Means of Two Log-Normal Distributions: A Likelihood Approach","year":2014,"lang":"en","type":"article","venue":"Journal of Statistical and Econometric Methods","topic":"Diverse Scientific and Engineering Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Simon Fraser University","funders":"","keywords":"Mathematics; Statistics; Normal distribution; Log-normal distribution; Algorithm; Applied mathematics","score_opus":0.04566328340804002,"score_gpt":0.3151711860487973,"score_spread":0.2695079026407573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187335251","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":[0.018814595,0.0003053355,0.9788725,0.0001789852,0.000041205003,0.00007256083,0.00013039458,0.000516641,0.0010678627],"genre_scores_gemma":[0.44440067,0.00061335985,0.5507625,0.00017603296,0.00013639119,0.0005112361,0.00088036974,0.00029254187,0.0022268682],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98874646,0.005962977,0.00059193664,0.0016979554,0.0026317001,0.00036890223],"domain_scores_gemma":[0.95270395,0.038818587,0.0026657046,0.0019385389,0.0034092215,0.0004640354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014896543,0.0010786924,0.0016613037,0.005423182,0.0009168333,0.0042471737,0.0022891718,0.0019691554,0.003376574],"category_scores_gemma":[0.070832565,0.00057066453,0.0016324967,0.0031512713,0.0021261699,0.004911417,0.0024256606,0.002442629,0.0010491962],"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":[0.0014071463,0.00047033007,0.040416155,0.0010935095,0.0008135314,0.001102616,0.0023550799,0.23475738,0.010013074,0.1381592,0.0051635234,0.56424844],"study_design_scores_gemma":[0.00008954853,0.0004317573,0.013241529,0.00017272029,0.00014122613,0.00090630155,0.000837891,0.84651816,0.0051634856,0.12622634,0.0060363575,0.00023469895],"about_ca_topic_score_codex":0.0015491343,"about_ca_topic_score_gemma":0.0012533863,"teacher_disagreement_score":0.014896543,"about_ca_system_score_codex":0.0012820703,"about_ca_system_score_gemma":0.0016456392,"threshold_uncertainty_score":0.07878137},"labels":[],"label_agreement":null},{"id":"W2308448763","doi":"","title":"Statistical Evaluation of Value at Risk Models for Estimating Agricultural Risk","year":2014,"lang":"en","type":"article","venue":"Journal of Statistical and Econometric Methods","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Value at risk; Skewness; Kurtosis; Econometrics; Statistics; EWMA chart; Mathematics; Parametric statistics; Economics; Computer science; Risk management; Control chart","score_opus":0.10150144080856038,"score_gpt":0.3545747350353332,"score_spread":0.2530732942267728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2308448763","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":[0.1633542,0.0010078988,0.8326825,0.0003602418,0.00005190938,0.00007995801,0.00015863334,0.00041451477,0.0018902862],"genre_scores_gemma":[0.87333167,0.00060274417,0.12449467,0.00009021288,0.00008865558,0.00014800915,0.00048911665,0.00010772815,0.0006472201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99264365,0.005460328,0.00026201626,0.00037586363,0.0011084541,0.00014980338],"domain_scores_gemma":[0.921883,0.07033293,0.002406456,0.0026811925,0.0023285686,0.00036784043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017211825,0.00089458155,0.00083292945,0.0018868105,0.00030807155,0.0012387607,0.001252854,0.001021931,0.00084304344],"category_scores_gemma":[0.061406597,0.0003756124,0.0008463741,0.0011788309,0.0007494475,0.002157617,0.0012526464,0.0012133134,0.00020737867],"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":[0.00017348073,0.00008265059,0.01176624,0.00006517916,0.00018689956,0.00007362672,0.000076109056,0.90835935,0.0008406964,0.02533188,0.00048600356,0.05255779],"study_design_scores_gemma":[0.000006299941,0.00006221948,0.000997084,0.000009158749,0.000010737234,0.000017997578,0.000010869439,0.9934627,0.00021267768,0.005049197,0.00015144565,0.000009718914],"about_ca_topic_score_codex":0.002350666,"about_ca_topic_score_gemma":0.0018008377,"teacher_disagreement_score":0.017211825,"about_ca_system_score_codex":0.00083726464,"about_ca_system_score_gemma":0.000854499,"threshold_uncertainty_score":0.09102589},"labels":[],"label_agreement":null},{"id":"W4385621449","doi":"10.47260/jsem/1241","title":"Generalized Additive Modelling of Dependent Frequency and Severity Distributions for Aggregate Claims","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical and Econometric Methods","topic":"Probability and Risk Models","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 Guelph","funders":"","keywords":"Nonparametric statistics; Econometrics; Generalized additive model; Generalized linear model; Aggregate (composite); Additive model; Frequentist inference; Variance (accounting); Mathematics; Statistics; Economics; Bayesian probability; Bayesian inference","score_opus":0.2765107885121423,"score_gpt":0.4546785589190189,"score_spread":0.17816777040687665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385621449","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":[0.19688307,0.00028535366,0.7985816,0.0005643086,0.000048889822,0.000096849595,0.00077910133,0.0004928471,0.0022679695],"genre_scores_gemma":[0.9364878,0.00022424242,0.05912443,0.0000756284,0.00006762155,0.00013026268,0.00068518985,0.00008266004,0.0031222343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952787,0.002685666,0.00020064648,0.00078145193,0.0007828524,0.00027069548],"domain_scores_gemma":[0.95598435,0.03432517,0.0039200564,0.0038620797,0.0015891179,0.0003192594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014052919,0.00069250935,0.0010819972,0.0028240695,0.00043443582,0.0027740505,0.0026446446,0.0017279423,0.0039973627],"category_scores_gemma":[0.043936297,0.00047501773,0.001864595,0.0025492448,0.0016877941,0.002313248,0.0014856241,0.003037724,0.00076086784],"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":[0.00021812881,0.00018383202,0.030896368,0.00019117547,0.00021828219,0.00054261566,0.0009697883,0.6469534,0.0017089009,0.2421414,0.0022291234,0.07374703],"study_design_scores_gemma":[0.0000129429745,0.000058922553,0.0057303505,0.000028247552,0.000024241177,0.00012015673,0.000101496196,0.9077765,0.00020648472,0.08509661,0.00081597903,0.000028100232],"about_ca_topic_score_codex":0.0053397194,"about_ca_topic_score_gemma":0.005060176,"teacher_disagreement_score":0.014052919,"about_ca_system_score_codex":0.0012232146,"about_ca_system_score_gemma":0.0006692389,"threshold_uncertainty_score":0.07431978},"labels":[],"label_agreement":null}]}