{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005273711,0.0006761924,0.001172665,0.001377766,0.0003405959,0.001203199,0.003194221,0.001620741,0.001824548],"category_scores_gemma":[0.01988346,0.0008307818,0.001048366,0.001661595,0.001448842,0.002556495,0.002166123,0.001573864,0.0005204895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008095222,"about_ca_system_score_gemma":0.001299554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002380447,"about_ca_topic_score_gemma":0.001936617,"domain_scores_codex":[0.9977224,0.001104973,0.0001124713,0.0005092253,0.0004281729,0.0001226487],"domain_scores_gemma":[0.9933317,0.004800017,0.0005708019,0.0005827664,0.0005567126,0.0001580536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004068228,0.0001939372,0.01429836,0.0004609126,0.0003035125,0.0003637129,0.0004211309,0.5579366,0.01325847,0.125044,0.00294405,0.2843685],"study_design_scores_gemma":[0.0000199053,0.00003984845,0.002044836,0.00001857294,0.00002873923,0.0001054645,0.00002033603,0.9748393,0.00134108,0.02045288,0.001054613,0.00003444139],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00606064,0.00008086758,0.993475,0.00004965491,0.000007254757,0.00001463727,0.00006306509,0.00008240941,0.00016631],"genre_scores_gemma":[0.2822251,0.000405771,0.7122655,0.0001640456,0.00009322022,0.0003112899,0.001231359,0.0001565018,0.003147202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005273711,"threshold_uncertainty_score":0.02789038,"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."}}