{"id":"W4224248376","doi":"10.1002/eap.2638","title":"Estimating animal abundance at multiple scales by spatially explicit capture–recapture","year":2022,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Ministry of Energy, Northern Development and Mines; Ministry of Natural Resources and Forestry","funders":"","keywords":"Estimator; Replicate; Statistics; Sampling (signal processing); Variance (accounting); Abundance (ecology); Poisson distribution; Distance sampling; Mark and recapture; Abundance estimation; Population; Mathematics; Econometrics; Ecology; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003830413,0.0004747762,0.000575977,0.0007676643,0.0003475255,0.0006070542,0.00110014,0.0003512228,0.0004589818],"category_scores_gemma":[0.01053702,0.0005809094,0.000626207,0.001016144,0.0005235625,0.001053798,0.0008515912,0.0003548932,0.0001452516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051831,"about_ca_system_score_gemma":0.0007916809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04143521,"about_ca_topic_score_gemma":0.1416494,"domain_scores_codex":[0.9984242,0.0007384851,0.00009370876,0.0004685608,0.0002136253,0.0000615188],"domain_scores_gemma":[0.9943147,0.002394078,0.001737236,0.001058296,0.0004268351,0.00006881788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002394976,0.000128278,0.7001467,0.0004001934,0.001754019,0.0001761717,0.0007928833,0.1723786,0.01469278,0.003150909,0.0005683302,0.1055717],"study_design_scores_gemma":[0.00005623849,0.0003424301,0.6435011,0.00006851874,0.0006121771,0.0003194845,0.0002814699,0.3412782,0.005632677,0.005142441,0.002654652,0.0001106539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6492359,0.0005784644,0.3474286,0.0001043632,0.000009965871,0.0001536116,0.0008895841,0.0002880069,0.001311397],"genre_scores_gemma":[0.908534,0.0001513552,0.09043241,0.00003639027,0.000007948695,0.0001063066,0.0004589064,0.00001482651,0.000257908],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04143521,"threshold_uncertainty_score":0.08238804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0093567104939255,"score_gpt":0.2164220870068073,"score_spread":0.2070653765128818,"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."}}