{"id":"W2907944377","doi":"10.1002/cjs.11477","title":"A new integrated likelihood for estimating population size in dependent dual‐record system","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Identifiability; Likelihood function; Computer science; Prior probability; Bayesian probability; Dual (grammatical number); Population size; Population; Statistics; Machine learning; Econometrics; Mathematics; Estimation theory; Artificial intelligence; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005081905,0.0001216968,0.0002647109,0.0002379314,0.0001182212,0.0000688,0.00008910382,0.00007738401,0.0001046443],"category_scores_gemma":[0.002465798,0.0001179806,0.00003895984,0.0001731833,0.00001796509,0.000123288,0.000004257305,0.0001351678,0.000005225675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005403087,"about_ca_system_score_gemma":0.0008261321,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01520663,"about_ca_topic_score_gemma":0.2216896,"domain_scores_codex":[0.9986361,0.00004884876,0.0007972809,0.00009684516,0.0001638073,0.0002571274],"domain_scores_gemma":[0.9979602,0.0004901157,0.0005576799,0.0001035306,0.0005267503,0.0003617544],"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":[0.0002332106,0.00007150695,0.1326666,0.001124953,0.0001677312,0.0003021739,0.0061443,0.001588611,0.00009270512,0.3169248,0.08079812,0.4598853],"study_design_scores_gemma":[0.00432101,0.0009770733,0.09379283,0.002312935,0.0003306649,0.000600166,0.001342816,0.4993786,0.00008579964,0.3925902,0.003500869,0.0007670217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08221913,0.00001793799,0.9158332,0.0001147852,0.001177088,0.0002562898,0.0002366277,0.00001121262,0.000133717],"genre_scores_gemma":[0.4871389,3.042619e-7,0.512459,0.00001762325,0.0002934547,0.000001466467,0.00001909767,0.00001563486,0.0000545252],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.49779,"threshold_uncertainty_score":0.9913512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03637881255100703,"score_gpt":0.2979550106058383,"score_spread":0.2615761980548312,"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."}}