{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01158183,0.001492246,0.001735497,0.00346903,0.001182366,0.001609124,0.002495313,0.0009266938,0.002238357],"category_scores_gemma":[0.03437042,0.0006276994,0.002477694,0.002061923,0.0008895479,0.002068569,0.002519364,0.00162145,0.0006405144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007384014,"about_ca_system_score_gemma":0.001747568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004489171,"about_ca_topic_score_gemma":0.007564179,"domain_scores_codex":[0.9934006,0.004825776,0.0002681322,0.0006058426,0.0006945533,0.0002051579],"domain_scores_gemma":[0.9832129,0.01330509,0.0008452423,0.001123173,0.001218437,0.0002951663],"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":[0.0003518526,0.00017136,0.03700868,0.0002134807,0.0009640098,0.0004200313,0.0003655751,0.6988729,0.003848328,0.02967475,0.001875721,0.2262334],"study_design_scores_gemma":[0.00002315286,0.0001017744,0.00263488,0.00001298867,0.00006992546,0.00006107098,0.00003799835,0.9832403,0.0006890554,0.01262095,0.0004721295,0.00003580569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02874922,0.00005705318,0.9700772,0.00007974972,0.00001563348,0.00007325158,0.0001154293,0.00043673,0.0003957203],"genre_scores_gemma":[0.546069,0.0001187145,0.451084,0.00006767506,0.00004952323,0.0002818453,0.001011735,0.000204145,0.001113351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01158183,"threshold_uncertainty_score":0.06125134,"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."}}