{"meta":{"query_hash":"cf219e5a0bfc","filters":{"venue":"Academic Journal of Applied Mathematical Sciences"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"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/cf219e5a0bfc","api":"https://metacan.xera.ac/api/v1/cohort?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,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.2746315057138788,"score_gpt":0.41780045981910546,"score_spread":0.14316895410522668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293282398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018631378,0.000015751104,0.9732951,0.005846477,0.00006451769,0.00031685788,0.00019358844,0.000022581806,0.0016137465],"genre_scores_gemma":[0.9791364,0.000008479667,0.020116571,0.0006259375,0.00003836077,0.000009946106,0.000020762107,0.0000082094,0.000035312947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976657,0.00007235474,0.00073403324,0.00022717101,0.0010825109,0.00021824877],"domain_scores_gemma":[0.99693793,0.0016671278,0.00069435046,0.0003762811,0.00008773937,0.00023654486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021383273,0.00012987264,0.00028353836,0.000059680497,0.00047913406,0.000025331974,0.0014094159,0.0000556541,0.0004527641],"category_scores_gemma":[0.004468624,0.00007610728,0.000045232704,0.00089230185,0.00043907444,0.00009777137,0.00033706974,0.0005919892,0.000011165137],"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":[0.000072090304,0.0001071899,0.000006404512,0.000038006263,0.00001622401,7.989613e-7,0.00017085421,0.019614026,0.00010040161,0.9694683,0.010309227,0.00009647824],"study_design_scores_gemma":[0.00036883444,0.00013154747,0.000007371103,0.00003458663,0.000091006776,0.00006591714,0.00046170916,0.14489135,0.00004295873,0.8531668,0.0006494332,0.00008843195],"about_ca_topic_score_codex":0.0000105996805,"about_ca_topic_score_gemma":0.0000015157141,"teacher_disagreement_score":0.96050507,"about_ca_system_score_codex":0.0001442164,"about_ca_system_score_gemma":0.0007462187,"threshold_uncertainty_score":0.5349683},"labels":[],"label_agreement":null}]}