{"id":"W4407899261","doi":"10.1080/19485565.2025.2465547","title":"Identifying the effects of large catastrophic shocks on the distribution of births using a combination of Benford’s law and the Vector Error Correction Model(VECM)","year":2025,"lang":"en","type":"article","venue":"Biodemography and Social Biology","topic":"Benford’s Law and Fraud Detection","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Advancing Health Outcomes","funders":"","keywords":"Benford's law; Econometrics; Statistics; Error correction model; Distribution (mathematics); Mathematics; Law; Cointegration; Mathematical analysis; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004987575,0.0004968003,0.0009553426,0.001790705,0.0004713722,0.001875669,0.00112486,0.001224367,0.002770548],"category_scores_gemma":[0.02038554,0.0004293718,0.0009203355,0.00203637,0.001028728,0.001310081,0.00114981,0.001518831,0.0003868609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014628,"about_ca_system_score_gemma":0.001197796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02473714,"about_ca_topic_score_gemma":0.01301878,"domain_scores_codex":[0.9971668,0.001622048,0.0001397476,0.0005026595,0.0002570372,0.000311656],"domain_scores_gemma":[0.9749427,0.01947109,0.003611732,0.001062665,0.0006220759,0.0002898102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002449409,0.0002296766,0.6167979,0.0001751856,0.000689136,0.001529922,0.000659405,0.2525754,0.0009679158,0.04772528,0.002545007,0.07586025],"study_design_scores_gemma":[0.00002774219,0.0001626529,0.07791904,0.00004734107,0.00008896866,0.0002598393,0.0003923805,0.9073001,0.0005381768,0.01155747,0.001663501,0.00004277492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8672894,0.0006693228,0.128022,0.0009704679,0.00007662916,0.0001060345,0.0007244939,0.000215974,0.00192571],"genre_scores_gemma":[0.9871055,0.0003755356,0.009413884,0.00006604788,0.00004704734,0.00005846537,0.0007074122,0.00001789944,0.002208163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02473714,"threshold_uncertainty_score":0.04918635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02565133824171748,"score_gpt":0.2994781892357359,"score_spread":0.2738268509940184,"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."}}