{"id":"W4390343103","doi":"10.5539/ijsp.v12n6p1","title":"Modelling Factors that Predict Differences in Childhood Mortality in Lagos Communities Using Prognostic Logistic and Poisson Regression Models","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tertiary Education Trust Fund","keywords":"Poisson regression; Covariate; Logistic regression; Child mortality; Demography; Regression analysis; Statistics; Poisson distribution; Medicine; Population; Environmental health; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001823948,0.0001267283,0.0002628543,0.0002957433,0.0001513725,0.0001490531,0.0002600497,0.00006500604,0.000007682592],"category_scores_gemma":[0.000249019,0.000104776,0.00003511188,0.0001823945,0.0003863133,0.0003369608,0.0001054297,0.0002671516,1.139354e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001103664,"about_ca_system_score_gemma":0.00009582061,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01008945,"about_ca_topic_score_gemma":0.006589684,"domain_scores_codex":[0.9980723,0.0003726406,0.0005041293,0.0001466403,0.000698222,0.0002060193],"domain_scores_gemma":[0.9987231,0.0005747274,0.0003069697,0.00008644321,0.0002347171,0.00007406727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002530155,0.0001208478,0.9584925,0.00004459733,0.00004026095,0.00002581387,0.0110172,0.01511575,5.663927e-7,0.01407796,0.000003664684,0.001035496],"study_design_scores_gemma":[0.0002278028,0.00002903772,0.663013,0.0002090378,0.00001645639,0.00000137352,0.003504371,0.1181669,6.339889e-7,0.2147509,0.000003931156,0.00007656941],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845573,0.0001477484,0.01431931,0.0001191744,0.0002948725,0.0002693087,0.0001692919,0.00001243826,0.0001105955],"genre_scores_gemma":[0.9962548,0.00163412,0.002033843,0.00001148208,0.00003769907,0.000004280731,0.00001444499,0.000006110726,0.000003212721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2954795,"threshold_uncertainty_score":0.9965025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1498504068284532,"score_gpt":0.3526818630123412,"score_spread":0.202831456183888,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}