{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007213086,0.0005460904,0.001153094,0.001080062,0.0003052729,0.0006432401,0.001369698,0.0007584561,0.0008604595],"category_scores_gemma":[0.03124826,0.0006606401,0.0007282302,0.001418509,0.000575729,0.001479157,0.0008515603,0.001020282,0.0002908452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003624275,"about_ca_system_score_gemma":0.0005768259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002572375,"about_ca_topic_score_gemma":0.002512865,"domain_scores_codex":[0.9973488,0.001897689,0.0001244584,0.0003358042,0.00021352,0.00007973661],"domain_scores_gemma":[0.985586,0.01238884,0.0009101693,0.0006738495,0.0003775223,0.00006358148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003154731,0.0001833222,0.07294422,0.0007518002,0.0007513278,0.0005290824,0.0005836174,0.5218275,0.00583804,0.04015981,0.002579667,0.3535361],"study_design_scores_gemma":[0.00004338268,0.00005859436,0.01409702,0.00005357915,0.00005174624,0.0001929207,0.00008395752,0.9429721,0.001554033,0.03940671,0.001449766,0.00003617276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02534612,0.0002975702,0.9736462,0.00007495129,0.000007650529,0.00002905497,0.0001098576,0.000172798,0.0003157241],"genre_scores_gemma":[0.5779247,0.0006279214,0.4185701,0.0001138124,0.00005569541,0.0002937522,0.001254601,0.00007749448,0.001081856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007213086,"threshold_uncertainty_score":0.03814691,"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."}}