{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003144621,0.0004127121,0.0006387539,0.0005773591,0.0007148126,0.0003160337,0.0003314578,0.000623183,0.0002761214],"category_scores_gemma":[0.001506963,0.0004022706,0.0002535874,0.0003882599,0.00005870709,0.0002646677,0.0007135334,0.001563829,0.000005834563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005773852,"about_ca_system_score_gemma":0.000263425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005387207,"about_ca_topic_score_gemma":0.0002543614,"domain_scores_codex":[0.9951876,0.0007472351,0.00080319,0.000897203,0.001700224,0.0006644814],"domain_scores_gemma":[0.9964078,0.001402287,0.0003664135,0.0007623912,0.000816478,0.0002446257],"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":[0.002089998,0.002451627,0.01532537,0.03191574,0.0008163576,0.00006401933,0.0487394,0.2501807,0.0000741924,0.2340247,0.103295,0.3110228],"study_design_scores_gemma":[0.0005827173,0.00007988479,0.004198203,0.0002063898,0.0000513242,0.000005717327,0.0003020836,0.5369571,0.000004890586,0.4565946,0.0007504518,0.0002665418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.519115,0.002511791,0.4524345,0.00503139,0.001214196,0.01489611,0.002507576,0.0007906289,0.001498823],"genre_scores_gemma":[0.8872499,0.0001023827,0.1037114,0.00005750625,0.0004079941,0.001639277,0.006028652,0.0001425276,0.0006603661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.368135,"threshold_uncertainty_score":0.9998429,"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."}}