{"id":"W4321599375","doi":"10.1111/csp2.12905","title":"Prioritizing populations based on recovery potential","year":2023,"lang":"en","type":"article","venue":"Conservation Science and Practice","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of British Columbia; Ministry of Forests; Alberta Biodiversity Monitoring Institute; University of Alberta","funders":"","keywords":"Weighting; Metric (unit); Variance (accounting); Baseline (sea); Population; Prioritization; Expert elicitation; Computer science; Environmental resource management; Habitat; Risk analysis (engineering); Statistics; Ecology; Business; Mathematics; Environmental science; Management science; Engineering; Biology; Marketing","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":[0.00260961,0.00006896446,0.00005674508,0.0001216009,0.0008031507,0.0001128765,0.0001314425,0.00004664003,0.0001418601],"category_scores_gemma":[0.005099847,0.0000697724,0.00001413505,0.001508653,0.0003585232,0.001777151,0.000072728,0.00009543569,0.0006814003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007883759,"about_ca_system_score_gemma":0.0001129949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001321587,"about_ca_topic_score_gemma":0.00003471574,"domain_scores_codex":[0.9987928,0.0001115617,0.0001471197,0.0002988351,0.0004541383,0.00019553],"domain_scores_gemma":[0.9989009,0.0006781939,0.0001022819,0.0001761134,0.00007333687,0.00006910515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004000184,0.0001709922,0.8222529,0.00001246433,0.000007944694,0.00005489383,0.0005733164,0.01163618,0.02080914,0.007744722,0.0904816,0.04585588],"study_design_scores_gemma":[0.0001634096,0.00006100039,0.903457,0.000006129932,0.00001041709,0.00001243278,0.0001619341,0.07020196,0.0000361385,0.001125994,0.02466719,0.00009632653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9185714,0.000003713929,0.001222424,0.06123515,0.0004306544,0.0001823824,0.000002584484,0.0001107247,0.01824101],"genre_scores_gemma":[0.9730496,0.00001408962,0.003036596,0.02329989,0.00003479708,0.0000194807,0.00001045631,0.000005133312,0.0005298934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08120422,"threshold_uncertainty_score":0.8758249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05535198606873188,"score_gpt":0.3168779080373612,"score_spread":0.2615259219686293,"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."}}