{"id":"W2054663330","doi":"10.1111/j.1541-0420.2008.01129.x","title":"A Multilevel Model for Continuous Time Population Estimation","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Estimation; Contingency table; Population; Population size; Estimator; Statistics; Computer science; Bayesian probability; Econometrics; Hierarchical database model; Statistical model; Data mining; Mathematics; Medicine","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":[0.0003301868,0.0001208024,0.0001886518,0.0006332669,0.0001059331,0.00004138634,0.00007497315,0.0001139406,0.00001711641],"category_scores_gemma":[0.001531325,0.0001184086,0.00007568333,0.0007031955,0.000006961735,0.0001741174,0.00000769392,0.00003949385,0.0000237978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008881724,"about_ca_system_score_gemma":0.00001421479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007412931,"about_ca_topic_score_gemma":9.350975e-7,"domain_scores_codex":[0.9990551,0.00001429171,0.0003563462,0.0001719124,0.000229925,0.0001723765],"domain_scores_gemma":[0.9991208,0.0002525105,0.0002117008,0.0001804509,0.0001791953,0.00005536386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009459796,0.0005716766,0.001825708,0.0001400001,0.00002613278,8.036302e-7,0.0004923448,0.02159711,0.001975778,0.1386705,0.01308161,0.8215238],"study_design_scores_gemma":[0.0004354459,0.00005324815,0.01537993,0.00001171924,0.00002669435,0.00000166359,0.0000013435,0.8552684,0.00006173358,0.1284926,0.0001385728,0.0001287184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1186739,0.00002266785,0.8799856,0.0002122429,0.00008162566,0.0005516366,0.00005915399,0.0001679789,0.0002452408],"genre_scores_gemma":[0.6884046,0.000001306814,0.3103797,0.00006515624,0.00004454356,0.00001097645,0.0002404961,0.00001246983,0.0008407983],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8336712,"threshold_uncertainty_score":0.4828562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09696894577694708,"score_gpt":0.3672199900365666,"score_spread":0.2702510442596195,"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."}}