{"id":"W2090853233","doi":"10.1111/j.1541-0420.2007.00767.x","title":"Multilist Population Estimation with Incomplete and Partial Stratification","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Simon Fraser University","funders":"","keywords":"Population stratification; Computer science; Stratification (seeds); Population; Statistics; Maximization; Population size; Mark and recapture; Estimation; Econometrics; Expectation–maximization algorithm; Data mining; Mathematics; Mathematical optimization; Maximum likelihood; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006074418,0.0001053544,0.0001161841,0.0005636443,0.0001344705,0.00005370346,0.00004147681,0.00007514453,0.00001402026],"category_scores_gemma":[0.0004418848,0.00009117277,0.00001635072,0.00127433,0.00003051041,0.0002053156,0.00001162101,0.00005460378,0.000005330348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006411797,"about_ca_system_score_gemma":0.000009336487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000104908,"about_ca_topic_score_gemma":0.00007465914,"domain_scores_codex":[0.9990425,0.00002201448,0.0003300831,0.0001754685,0.0002856842,0.0001442286],"domain_scores_gemma":[0.9991506,0.0002841607,0.0002173023,0.0001639176,0.0001140465,0.00007002048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001609818,0.0002606954,0.2466895,0.0002365844,0.00003209706,0.00000453962,0.0006016923,0.0003203506,0.001544119,0.214466,0.0002477762,0.5354357],"study_design_scores_gemma":[0.0004808927,0.00007755132,0.867169,0.00001933256,0.00003364259,0.00001155393,0.00003298935,0.1220115,0.0004461705,0.009163497,0.0003803632,0.0001735778],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5964854,0.00001895095,0.4028468,0.00006562262,0.00006413037,0.0002104737,0.000006824254,0.00007380501,0.0002279447],"genre_scores_gemma":[0.8733544,0.000002489535,0.1263731,0.00001382059,0.00005348449,0.000004106997,0.0001531899,0.00001273869,0.00003267432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6204795,"threshold_uncertainty_score":0.3717918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.081416258894013,"score_gpt":0.3580471631329333,"score_spread":0.2766309042389203,"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."}}