{"id":"W4309934729","doi":"10.21203/rs.3.rs-2307024/v1","title":"Penalized empirical likelihood estimation and EM algorithms for closed-population capture--recapture models","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Census and Population Estimation","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Estimator; Mark and recapture; Expectation–maximization algorithm; Statistic; Maximum likelihood; Population; Algorithm; Computer science; Abundance (ecology); Statistics; Estimation; Estimation theory; Mathematics; Econometrics; Biology; Engineering; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00867686,0.001633673,0.002360587,0.001864159,0.001060489,0.002223287,0.006161468,0.003110563,0.005637184],"category_scores_gemma":[0.07553267,0.002288258,0.001655902,0.002737346,0.002452446,0.00514193,0.004025322,0.005183171,0.001659873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00192898,"about_ca_system_score_gemma":0.002154018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008126438,"about_ca_topic_score_gemma":0.008178122,"domain_scores_codex":[0.9961556,0.002593952,0.0001985961,0.0006173548,0.0002892986,0.0001452737],"domain_scores_gemma":[0.9420716,0.05170364,0.001534535,0.002418426,0.00174247,0.0005293235],"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":[0.0001933819,0.0001241253,0.002008796,0.0002740043,0.0001946772,0.0001200182,0.0002967385,0.730332,0.0006069901,0.1725131,0.006345344,0.08699098],"study_design_scores_gemma":[0.00001712936,0.000009273542,0.00021523,0.00001829711,0.0000120855,0.00002704426,0.00001264011,0.9255577,0.0001571669,0.07313134,0.0008245346,0.00001754744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002949088,0.000301175,0.9957418,0.0002633246,0.00002820321,0.00001870404,0.00009687169,0.0002094623,0.0003913472],"genre_scores_gemma":[0.1709894,0.001252182,0.8125114,0.0003415886,0.0004988207,0.0005845558,0.00219609,0.0008720686,0.01075387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00867686,"threshold_uncertainty_score":0.04588813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.212287666748234,"score_gpt":0.4844880234155764,"score_spread":0.2722003566673423,"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."}}