{"id":"W3093170289","doi":"10.2981/wlb.00697","title":"Comparison of grizzly bear hair‐snag and scat sampling along roads to inform wildlife population monitoring","year":2020,"lang":"en","type":"article","venue":"Wildlife Biology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Insight Electronics (Canada)","funders":"Norsk institutt for Bioøkonomi; Forest Resource Improvement Association of Alberta; Alberta Environment and Parks","keywords":"Grizzly Bears; Sampling (signal processing); Snag; Wildlife; Population; Geography; Ecology; Environmental science; Biology; Habitat; Ursus; Computer science; Environmental health; Medicine","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":[0.0002176799,0.0001665248,0.0003319356,0.00005044664,0.0001987397,0.00001440517,0.0001992022,0.0002028319,0.00008139998],"category_scores_gemma":[0.0002447822,0.0001657585,0.00004313195,0.0003112907,0.000148117,0.0002230211,0.0002440838,0.0001622957,0.0001095565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006378523,"about_ca_system_score_gemma":0.00001269298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004949163,"about_ca_topic_score_gemma":0.00009558508,"domain_scores_codex":[0.9986737,0.0000667385,0.0004773125,0.0003620694,0.0001298098,0.0002903193],"domain_scores_gemma":[0.9993286,0.0001094514,0.0001895824,0.0001636881,0.00001503925,0.0001936164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005354275,0.00002338406,0.9894481,0.00001047174,0.0000139615,3.914489e-7,0.0009297187,0.001023206,0.001958878,0.0002267315,0.0009290277,0.005382605],"study_design_scores_gemma":[0.0002506655,0.0002693601,0.9872864,0.00001422901,0.00001540124,0.000002290527,0.0001927408,0.0008256386,0.0002777902,0.0001277978,0.01056723,0.0001704367],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990572,0.00004456915,0.0009353039,0.007604419,0.0002768011,0.0002564822,0.000006010932,0.00006005611,0.0002443346],"genre_scores_gemma":[0.9923568,0.00001041462,0.003621645,0.003741461,0.0001809765,0.00001757954,0.00003621805,0.00001236104,0.00002257011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009638199,"threshold_uncertainty_score":0.6759437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05130260619533972,"score_gpt":0.3122252824052885,"score_spread":0.2609226762099487,"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."}}