{"id":"W4315621577","doi":"10.1093/biosci/biac105","title":"Evaluation Options for Wildlife Management and Strengthening of Causal Inference","year":2023,"lang":"en","type":"article","venue":"BioScience","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Canberra","keywords":"Adaptive management; Causal inference; Wildlife; Inference; Wildlife management; Statistical inference; Threatened species; Environmental resource management; Population; Risk analysis (engineering); Computer science; Business; Economics; Ecology; Econometrics; Biology; Mathematics; Artificial intelligence; Statistics; Habitat","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3516394,0.002077585,0.00257991,0.005662539,0.00234781,0.007730007,0.004990917,0.00642402,0.01052282],"category_scores_gemma":[0.5478687,0.001090079,0.003666521,0.003508006,0.0106236,0.01501059,0.00639031,0.006216748,0.0003516365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007487399,"about_ca_system_score_gemma":0.01005476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003143133,"about_ca_topic_score_gemma":0.004400098,"domain_scores_codex":[0.6661927,0.2985063,0.0108731,0.00700575,0.01533723,0.00208492],"domain_scores_gemma":[0.276314,0.6637217,0.02388546,0.01972407,0.01387523,0.002479595],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001811119,0.0005064445,0.01974972,0.002636849,0.001760426,0.0004800284,0.001207448,0.09466203,0.0005554301,0.7114165,0.003903474,0.1613105],"study_design_scores_gemma":[0.0004735395,0.0004367262,0.002670398,0.001562488,0.0005961506,0.0001174886,0.0003975099,0.1079955,0.0009635849,0.8794322,0.005214477,0.0001399155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04616027,0.004549813,0.8675798,0.04622276,0.0005662328,0.002840782,0.0006622439,0.0004283545,0.03098981],"genre_scores_gemma":[0.6591139,0.0009396156,0.3341144,0.002359458,0.000285025,0.002140989,0.0001634549,0.00003579748,0.0008474046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6483606,"threshold_uncertainty_score":0.7995441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04676600689209433,"score_gpt":0.3012977593474533,"score_spread":0.254531752455359,"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."}}