{"id":"W4394756346","doi":"10.1111/2041-210x.14320","title":"Using camera traps and N‐mixture models to estimate population abundance: Model selection really matters","year":2024,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western Forest Products; University of Northern British Columbia","funders":"Habitat Conservation Trust Foundation; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Abundance (ecology); Statistics; Sampling (signal processing); Population; Sample size determination; Model selection; Aerial survey; Selection (genetic algorithm); Statistical model; Scale (ratio); Population model; Abundance estimation; Mathematics; Ecology; Geography; Biology; Computer science; Cartography; 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":[0.0007129961,0.00009998061,0.0001254339,0.0001097898,0.0001766353,0.00002021338,0.00003628824,0.0001965892,0.00002563625],"category_scores_gemma":[0.00003120718,0.0001041681,0.00001444978,0.0002544725,0.00007475475,0.0004134492,0.00004509976,0.000169759,0.000005355647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003063768,"about_ca_system_score_gemma":0.00001595505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004819616,"about_ca_topic_score_gemma":0.00196374,"domain_scores_codex":[0.9990153,0.0002385415,0.0001780962,0.0003254625,0.00005066905,0.0001919623],"domain_scores_gemma":[0.9997726,0.00008847445,0.00003241097,0.00005724442,0.000005484931,0.00004377069],"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":[0.00003829989,0.00001567708,0.4600036,0.00001982973,0.000006808452,0.000001718269,0.0005529462,0.5210773,0.006148331,0.001888623,0.0002384348,0.01000835],"study_design_scores_gemma":[0.00005268914,0.0000225776,0.4443873,0.000009088364,0.00001141559,0.0000181873,0.00001201337,0.5181949,0.00001160902,0.03720494,0.00001792831,0.0000574077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5909234,0.00004740014,0.4078142,0.0008481314,0.0001338251,0.0001206219,0.000001029927,0.00002979533,0.00008162752],"genre_scores_gemma":[0.7351965,0.0000136844,0.2641114,0.0005386204,0.00001560253,0.00002352437,0.000003932905,0.000007174713,0.0000895069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1442732,"threshold_uncertainty_score":0.424785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03321383407152281,"score_gpt":0.3558542537028732,"score_spread":0.3226404196313504,"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."}}