{"id":"W4406299953","doi":"10.1093/aje/kwaf004","title":"Model selection and model robustness for population size estimation in 2-sample capture-recapture studies","year":2025,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Simon Fraser University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mark and recapture; Statistics; Estimator; Maximum likelihood; Econometrics; Model selection; Sample size determination; Selection (genetic algorithm); Population; Estimation; Robustness (evolution); Population size; Mathematics; Computer science; Biology; Demography; Economics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2016571,0.001636321,0.003197327,0.003876584,0.001774454,0.003401015,0.004103903,0.002613347,0.001546076],"category_scores_gemma":[0.5144711,0.00117881,0.003985063,0.002246319,0.003528109,0.003187481,0.002906039,0.00428426,0.00023408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001757271,"about_ca_system_score_gemma":0.002061254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005221188,"about_ca_topic_score_gemma":0.003968468,"domain_scores_codex":[0.8579795,0.1258248,0.005480053,0.006704969,0.003193959,0.0008167552],"domain_scores_gemma":[0.3810429,0.5861994,0.01048662,0.01636165,0.005049457,0.0008600001],"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":[0.002446614,0.0003027215,0.1491045,0.00176354,0.01255751,0.002027301,0.003271329,0.6697306,0.003519377,0.05448245,0.003191052,0.09760301],"study_design_scores_gemma":[0.0002342728,0.000572657,0.01933847,0.0003659354,0.001172298,0.0005475724,0.000368488,0.8954499,0.001659728,0.07805191,0.002038195,0.0002004109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1035445,0.00159376,0.8917344,0.0008764298,0.0001508003,0.00045089,0.0002991087,0.0005722182,0.0007779013],"genre_scores_gemma":[0.7785467,0.0003946075,0.2183187,0.0005300901,0.0001116125,0.0008931304,0.0006697826,0.0002248249,0.0003105129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2016571,"threshold_uncertainty_score":0.9844989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09423458415095483,"score_gpt":0.4136214191051371,"score_spread":0.3193868349541822,"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."}}