{"id":"W2593061628","doi":"10.1002/ajp.22647","title":"An empirical evaluation of camera trapping and spatially explicit capture‐recapture models for estimating chimpanzee density","year":2017,"lang":"en","type":"article","venue":"American Journal of Primatology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Max-Planck-Gesellschaft","keywords":"Mark and recapture; Camera trap; Density estimation; Sampling (signal processing); Abundance (ecology); Statistics; Estimator; Computer science; Population; Ecology; Mathematics; Biology; Habitat; Computer vision; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05244606,0.001380483,0.001120476,0.001364836,0.0004760498,0.0009501794,0.002042121,0.001343635,0.0007688946],"category_scores_gemma":[0.1075859,0.0009717252,0.001049588,0.0010816,0.001071468,0.002330642,0.001424069,0.001042024,0.0001482251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122625,"about_ca_system_score_gemma":0.0008753547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00916539,"about_ca_topic_score_gemma":0.008235455,"domain_scores_codex":[0.9779086,0.01850865,0.000702816,0.001693502,0.0009992431,0.0001871342],"domain_scores_gemma":[0.7168466,0.2570469,0.009592064,0.01148801,0.004590098,0.0004362229],"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":[0.001064005,0.0004900852,0.2639534,0.0005309361,0.002440577,0.0001833585,0.0006056409,0.5943898,0.001270504,0.005632737,0.0008149762,0.128624],"study_design_scores_gemma":[0.00009023399,0.0009250709,0.03942256,0.0000996743,0.0002489623,0.0002168297,0.0001304224,0.956223,0.0006454323,0.001318812,0.0006134164,0.00006552717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8226198,0.002722742,0.1703138,0.0005332662,0.00004464834,0.0003300791,0.0005535734,0.0002417428,0.002640399],"genre_scores_gemma":[0.9256688,0.000593145,0.07254209,0.0000951526,0.00003244658,0.0002303175,0.0003813342,0.00004420136,0.0004125227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05244606,"threshold_uncertainty_score":0.2773646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492108320280767,"score_gpt":0.3253024074027996,"score_spread":0.2903813241999919,"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."}}