{"id":"W2922060970","doi":"10.1002/ecs2.2586","title":"An integrated model decomposing the components of detection probability and abundance in unmarked populations","year":2019,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nature Conservancy of Canada; Nature Conservancy; North Carolina State University; University of California, Santa Cruz; University of California","keywords":"Abundance (ecology); Distance sampling; Sampling (signal processing); Statistics; Population; Statistical power; Ecology; Mathematics; Computer science; Biology; Demography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005195208,0.001056291,0.001675911,0.001625537,0.0005880461,0.003086635,0.00520407,0.002083578,0.004639775],"category_scores_gemma":[0.01037822,0.001365112,0.001416729,0.001716407,0.002124231,0.004396789,0.002333136,0.002006787,0.0007150754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002392828,"about_ca_system_score_gemma":0.00182366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01905814,"about_ca_topic_score_gemma":0.01370141,"domain_scores_codex":[0.9982584,0.0005227263,0.00008126113,0.00066339,0.0002210837,0.0002531333],"domain_scores_gemma":[0.9939454,0.003379733,0.00121126,0.0003292724,0.0007478097,0.0003864783],"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":[0.0001555835,0.000184716,0.02305872,0.00008017512,0.0002451055,0.0003488218,0.0004273546,0.909522,0.001516222,0.05062561,0.0007851223,0.01305055],"study_design_scores_gemma":[0.00001598068,0.0000368764,0.001723971,0.00001069242,0.00004665769,0.00004357531,0.00003934033,0.9897859,0.00008804201,0.007964355,0.000228373,0.00001617898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3778244,0.0004068831,0.6120182,0.001012459,0.00007794509,0.0002185693,0.001220004,0.0003601815,0.006861253],"genre_scores_gemma":[0.961426,0.0002535847,0.02641711,0.0001563808,0.00005137295,0.0003143456,0.000604152,0.00006513384,0.01071204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01905814,"threshold_uncertainty_score":0.03789443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721309682657944,"score_gpt":0.2324613433394549,"score_spread":0.2152482465128754,"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."}}