{"id":"W2016935685","doi":"10.1111/j.1541-0420.2010.01421.x","title":"Multistate Mark-Recapture Model Selection Using Score Tests","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Model selection; Selection (genetic algorithm); Computer science; Set (abstract data type); Mark and recapture; Simple (philosophy); Data set; Statistics; Machine learning; Data mining; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000271844,0.000114715,0.0001196426,0.0007293985,0.0001221265,0.00004782438,0.00007655489,0.0001514385,0.00004899827],"category_scores_gemma":[0.001044571,0.0001078689,0.00004539114,0.002165972,0.00002032191,0.0001362859,0.00002294887,0.0001784708,0.00001506676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006206676,"about_ca_system_score_gemma":0.0000402264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005062991,"about_ca_topic_score_gemma":0.00006763184,"domain_scores_codex":[0.9991766,0.00001500898,0.0002296675,0.0001647717,0.0002430034,0.0001709036],"domain_scores_gemma":[0.9992542,0.0001506253,0.0001547384,0.000167581,0.0002026939,0.0000701052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009465819,0.001094449,0.2232426,0.0007112171,0.0001069911,0.00001232489,0.001290229,0.01848428,0.4729847,0.08594691,0.02089699,0.1751346],"study_design_scores_gemma":[0.0002515925,0.00001368632,0.01346815,0.00001200593,0.00002975879,0.00001857315,0.000004200056,0.9706312,0.001053493,0.01334253,0.0009988867,0.0001759442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8586941,0.00001669922,0.1401535,0.00004553168,0.0004006083,0.0001704933,0.00002553863,0.0001114507,0.0003820572],"genre_scores_gemma":[0.7504354,0.000002987685,0.2490436,0.00002291335,0.0001178458,0.000002511145,0.00002151722,0.00002087556,0.0003324441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9521469,"threshold_uncertainty_score":0.4398768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1300206548474346,"score_gpt":0.3704660671891699,"score_spread":0.2404454123417353,"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."}}