{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":1,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"cf219e5a0bfc","filters":{"venue":"Academic Journal of Applied Mathematical Sciences"}},"results":[{"id":"W4293282398","doi":"10.32861/ajams.83.42.68","title":"On Bivariate Modeling of the COVID-19 Data with a New Type I Half-Logistic Inverse Weibull Distribution","year":2022,"lang":"en","type":"article","venue":"Academic Journal of Applied Mathematical Sciences","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Bivariate analysis; Weibull distribution; Univariate; Statistics; Goodness of fit; Quantile; Mathematics; Bivariate data; Logistic distribution; Econometrics; Log-logistic distribution; Logistic regression; Applied mathematics; Probability distribution; Distribution fitting; Multivariate statistics","authors":[{"name":"Ahmed Elhassanein","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2746315057138788,"gpt":0.4178004598191055,"spread":0.1431689541052267,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005261211,0.0006869291,0.0009557893,0.001560189,0.0004449993,0.001714219,0.001968513,0.001062321,0.002331764],"category_scores_gemma":[0.01229382,0.000365807,0.001331765,0.002408267,0.001286437,0.001948806,0.001701376,0.002134779,0.0005372664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168618,"about_ca_system_score_gemma":0.0009119704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004916735,"about_ca_topic_score_gemma":0.002996683,"domain_scores_codex":[0.9979011,0.001093623,0.00008507445,0.0002996283,0.0004420431,0.0001785947],"domain_scores_gemma":[0.99406,0.003704833,0.0008492396,0.0006352566,0.0006264726,0.0001240733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001642998,0.00007851026,0.01924361,0.0001794898,0.0001063611,0.0005630875,0.0004712777,0.6939716,0.001944893,0.2285004,0.001803782,0.05297258],"study_design_scores_gemma":[0.000008552176,0.00006268418,0.002932285,0.00002786604,0.00002802758,0.000191662,0.0001061821,0.9643331,0.0004776265,0.02995981,0.001833817,0.0000384988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05831023,0.0003919767,0.937276,0.0004018531,0.00004930503,0.000063539,0.0002667236,0.0001289616,0.003111371],"genre_scores_gemma":[0.8453684,0.00146897,0.1466347,0.000171032,0.0001526513,0.0002931155,0.0007493159,0.0001214657,0.005040369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005261211,"threshold_uncertainty_score":0.02782428,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}