{"id":"W4310941135","doi":"10.1002/eap.2787","title":"A genetic warning system for a hierarchically structured wildlife monitoring framework","year":2022,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"U.S. Bureau of Land Management; U.S. Geological Survey","keywords":"Population; Genetic diversity; Ecology; Conservation genetics; Effective population size; Geography; Environmental resource management; Biodiversity; Biology; Microsatellite; Environmental science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001638282,0.00009985744,0.0001202568,0.00002105819,0.0008864818,0.00002907167,0.0003669061,0.00004379645,0.0007809121],"category_scores_gemma":[0.00002445262,0.0000859355,0.00006933692,0.0002407596,0.00005384066,0.00002306891,0.0003955358,0.0002000442,0.00008895421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002223794,"about_ca_system_score_gemma":0.000005674975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000739249,"about_ca_topic_score_gemma":0.00000177496,"domain_scores_codex":[0.9989291,0.00005292228,0.0001939308,0.0003518026,0.0002135128,0.0002587025],"domain_scores_gemma":[0.999431,0.0001689199,0.00007270566,0.0002288406,0.0000040194,0.00009452504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001021077,0.0009630355,0.6443469,0.0001179155,0.0001104468,0.00002224006,0.0008429434,0.1753822,0.002303795,0.04525946,0.0168702,0.1136788],"study_design_scores_gemma":[0.0003030772,0.0001714206,0.5879149,0.000005058502,0.00003026907,0.000008468608,0.0003425512,0.002986825,0.00001668703,0.01432054,0.3936627,0.0002375527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7211658,0.00005802274,0.267191,0.002786139,0.0003068505,0.002883051,0.00006009195,0.0003726123,0.005176391],"genre_scores_gemma":[0.9494109,0.000003492412,0.04340861,0.0004174163,0.0001875543,0.006307462,0.000009360631,0.00001091684,0.0002443586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3767925,"threshold_uncertainty_score":0.855044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105808502634153,"score_gpt":0.2324715248437527,"score_spread":0.2218906745803374,"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."}}