{"id":"W2039233247","doi":"10.1002/cjs.5550360106","title":"Automatic generation of multistate capture‐recapture models","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Stability (learning theory); Mark and recapture; Artificial intelligence; Machine learning; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001831009,0.00006812521,0.0001428519,0.0000576665,0.0001167362,0.000006845487,0.0001201933,0.00005945362,0.001261022],"category_scores_gemma":[0.0001247474,0.00006339492,0.00002716803,0.00009004761,0.0001909422,0.0001708537,0.000005435591,0.0001197816,0.000025911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001303382,"about_ca_system_score_gemma":0.0002952097,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004389341,"about_ca_topic_score_gemma":0.03875634,"domain_scores_codex":[0.9992195,0.00005432592,0.0003741821,0.00006556447,0.0001501715,0.0001362589],"domain_scores_gemma":[0.9992915,0.00004797419,0.000300781,0.00008692926,0.00006332083,0.0002095014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000132866,0.00004922901,0.4523335,0.0000237282,0.00006707578,0.000823473,0.007290614,0.08991958,0.0007459871,0.004562045,0.432666,0.0115055],"study_design_scores_gemma":[0.0005871798,0.0001517289,0.6896491,0.00002125291,0.00005333508,0.0005528234,0.0001544832,0.2988681,0.0001540417,0.006677397,0.002922358,0.0002081893],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9363163,0.00005353192,0.06222996,0.0002739121,0.0002715239,0.00006067192,0.0001201365,0.000002033271,0.0006719931],"genre_scores_gemma":[0.9607891,0.00001456771,0.03850633,0.0003433429,0.00004099768,6.830207e-7,0.0000127642,0.00000641239,0.0002857605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4297436,"threshold_uncertainty_score":0.999652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02883306888446103,"score_gpt":0.2032933009510352,"score_spread":0.1744602320665742,"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."}}