{"id":"W4387566498","doi":"10.1002/ecs2.4669","title":"Accounting for heterogeneous density and detectability in spatially explicit capture–recapture studies of carnivores","year":2023,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Mark and recapture; Akaike information criterion; Ursus; Statistics; Robustness (evolution); Population; Econometrics; Variation (astronomy); Wildlife; Ecology; Population size; Covariate; Computer science; Mathematics; Biology","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":[0.0002858078,0.00008063265,0.0001574087,0.00001010101,0.00007888419,0.00000510645,0.00007174315,0.00007933132,0.0001065358],"category_scores_gemma":[0.0002208151,0.00007562247,0.00002897736,0.0001369526,0.00007995724,0.000084954,0.0001075803,0.00006267062,0.0000266782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004877746,"about_ca_system_score_gemma":0.000007603483,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000423263,"about_ca_topic_score_gemma":0.03150336,"domain_scores_codex":[0.9993592,0.00003468248,0.0001635443,0.0002147951,0.00006893653,0.0001588264],"domain_scores_gemma":[0.9996083,0.0001786058,0.00006910237,0.0001108626,0.00001334985,0.00001981287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002501018,0.0000143569,0.9925268,0.00003787369,0.00001248199,0.000003354859,0.001196836,0.001456838,0.001010457,0.00001068128,0.001143971,0.002561312],"study_design_scores_gemma":[0.0002226282,0.00003870417,0.9933085,0.00001091332,0.000009015498,0.000002062776,0.0008334228,0.001806246,0.001746368,0.001779554,0.0001552324,0.00008738077],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989337,0.0001285311,0.00003160935,0.0002996403,0.00008465951,0.0002481838,0.000003998667,0.00002758623,0.0002421159],"genre_scores_gemma":[0.9993297,0.00002280865,0.0003564126,0.0001271507,0.00001690062,0.00004274861,0.000002884554,0.000006305249,0.0000950779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0310801,"threshold_uncertainty_score":0.9861692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01692109474103759,"score_gpt":0.2469496834747764,"score_spread":0.2300285887337388,"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."}}