{"id":"W4366293207","doi":"10.1002/jwmg.22412","title":"Causality and wildlife management","year":2023,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Canberra","keywords":"Wildlife; Causality (physics); Wildlife management; Causal inference; Environmental resource management; Inference; Set (abstract data type); Geography; Computer science; Ecology; Risk analysis (engineering); Environmental planning; Business; Econometrics; Biology; Environmental science; Economics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.03346392,0.0007455088,0.001086492,0.005641914,0.00211545,0.005075975,0.001781634,0.002821329,0.01392638],"category_scores_gemma":[0.09652727,0.000453325,0.001552292,0.003179112,0.01348027,0.006250309,0.0041707,0.003474385,0.000568214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003384067,"about_ca_system_score_gemma":0.004468617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004011459,"about_ca_topic_score_gemma":0.003338095,"domain_scores_codex":[0.9741462,0.01866965,0.001651954,0.002411125,0.002461775,0.0006593078],"domain_scores_gemma":[0.8479303,0.120249,0.01788529,0.004811343,0.007173718,0.001950382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001125402,0.0001188945,0.04964463,0.00227849,0.0007495659,0.0005395202,0.001825505,0.008324138,0.000184876,0.8087095,0.005852522,0.1216599],"study_design_scores_gemma":[0.00003530449,0.00006957017,0.009214933,0.001853317,0.0001696203,0.0002804079,0.001143068,0.003048657,0.0001763747,0.9588323,0.0251289,0.00004757444],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1144223,0.1628204,0.3391879,0.1662326,0.006677587,0.001074748,0.001447635,0.0004708038,0.207666],"genre_scores_gemma":[0.9340166,0.02828686,0.02572226,0.005505492,0.001545549,0.0003027163,0.0002701883,0.00005517449,0.004295054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03346392,"threshold_uncertainty_score":0.1769763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519453335633769,"score_gpt":0.2401618561686791,"score_spread":0.2249673228123414,"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."}}