{"id":"W3042637318","doi":"10.1111/biom.13334","title":"Maximum likelihood abundance estimation from capture‐recapture data when covariates are missing at random","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Statistics; Estimator; Covariate; Mathematics; Confidence interval; Missing data; Coverage probability; Restricted maximum likelihood; Mark and recapture; Imputation (statistics); Point estimation; Maximum likelihood; Population","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":[0.0003723977,0.0002364349,0.0003786131,0.0002837004,0.0002005559,0.0001817029,0.0004504712,0.0002081917,0.000322984],"category_scores_gemma":[0.00423786,0.0002193644,0.00006631644,0.001531224,0.00003181201,0.0003641108,0.0002524669,0.0001623315,0.0001306464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001157528,"about_ca_system_score_gemma":0.00004194796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001559635,"about_ca_topic_score_gemma":0.00002734642,"domain_scores_codex":[0.9981451,0.00008384715,0.0005114268,0.0004937154,0.0005117052,0.0002542145],"domain_scores_gemma":[0.9976748,0.0007999901,0.0005040463,0.0006726896,0.0001511632,0.0001972869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001135561,0.0008298243,0.05275904,0.00170517,0.0006011344,0.0000953327,0.01313699,0.001816805,0.006293729,0.005527078,0.6196411,0.2964582],"study_design_scores_gemma":[0.004056678,0.00004894418,0.01020214,0.0001856932,0.0003394427,0.0000101267,0.0001272409,0.7924348,0.0006872782,0.1280209,0.06312315,0.0007635716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04522021,0.002650436,0.9395046,0.008786164,0.0009382079,0.0006387552,0.001437357,0.0004113015,0.0004130146],"genre_scores_gemma":[0.5784954,0.00004319347,0.4165761,0.0008612064,0.0004862408,0.000007116099,0.003377041,0.00006467408,0.00008901324],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.790618,"threshold_uncertainty_score":0.8945421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1102205926276703,"score_gpt":0.3219416116890971,"score_spread":0.2117210190614268,"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."}}