{"id":"W4387974195","doi":"10.1002/env.2827","title":"On the identifiability of the trinomial model for mark‐recapture‐recovery studies","year":2023,"lang":"en","type":"article","venue":"Environmetrics","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trinomial; Covariate; Identifiability; Inference; Econometrics; Joint probability distribution; Statistics; Statistical inference; Mathematics; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001029674,0.00009220126,0.0001194351,0.00004378737,0.0002491217,0.000006759391,0.0003261265,0.0000762182,0.00015693],"category_scores_gemma":[0.001659455,0.00005402614,0.0001215379,0.0006336098,0.0003404799,0.00007469932,0.0002480426,0.0001168139,0.0002094362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207455,"about_ca_system_score_gemma":0.000008484377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008977353,"about_ca_topic_score_gemma":0.00002472336,"domain_scores_codex":[0.9990948,0.00009605996,0.0001967702,0.0002165881,0.0002252026,0.0001705345],"domain_scores_gemma":[0.9977291,0.001747073,0.000114599,0.0003865187,0.000004202312,0.00001848521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001608615,0.0002189034,0.4650364,0.00002972058,0.00009551982,6.702624e-7,0.0009013979,0.3079491,0.0007340664,0.002983205,0.2142833,0.007606939],"study_design_scores_gemma":[0.0002064239,0.00004619873,0.9338681,0.000003286279,0.00002856935,2.523317e-7,0.0001103989,0.03745967,0.0004251816,0.02620516,0.001556494,0.00009030817],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955516,0.00002713194,0.0008527954,0.002119445,0.0003891038,0.0004374141,0.00002630801,0.00001685087,0.0005793197],"genre_scores_gemma":[0.9934789,0.00006476109,0.000120143,0.0007763739,0.00002658639,0.00008346158,0.000003130608,0.000008720702,0.005437954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4688317,"threshold_uncertainty_score":0.2691948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419280582766934,"score_gpt":0.2525691903345914,"score_spread":0.208376384506922,"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."}}