{"id":"W2791249411","doi":"10.1126/sciadv.aao0167","title":"Hallmarks of science missing from North American wildlife management","year":2018,"lang":"en","type":"article","venue":"Science Advances","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"Raincoast Conservation Foundation; Tula Foundation; University of Victoria; Simon Fraser University","funders":"Hakai Institute; Pacific Salmon Foundation; Tula Foundation; Simon Fraser University; Wilburforce Foundation","keywords":"Wildlife; Wildlife management; State (computer science); State management; Geography; Environmental resource management; Data science; Computer science; Biology; Ecology; Environmental science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.250181,0.0006836519,0.002365009,0.02079407,0.01839635,0.02734314,0.004060734,0.006978541,0.002154866],"category_scores_gemma":[0.3389803,0.001376878,0.001078787,0.01491016,0.06027484,0.0182215,0.01376783,0.01367275,0.0003358331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03006711,"about_ca_system_score_gemma":0.130419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06241972,"about_ca_topic_score_gemma":0.1242897,"domain_scores_codex":[0.779409,0.0993978,0.02788374,0.01419971,0.0736139,0.005495839],"domain_scores_gemma":[0.3810971,0.3798134,0.0557736,0.06393573,0.10724,0.01214014],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00008471331,0.0001463321,0.02440797,0.002230413,0.0001568837,0.0002580494,0.0190879,0.00104452,0.0008889278,0.7726385,0.03374331,0.1453124],"study_design_scores_gemma":[0.0000592954,0.000127685,0.03273471,0.00364063,0.0001629964,0.0003254474,0.01001435,0.00157331,0.001013348,0.7207156,0.2294945,0.0001382383],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04133509,0.02342487,0.08233485,0.6583316,0.00272477,0.001145987,0.00086416,0.0004094201,0.1894292],"genre_scores_gemma":[0.7529758,0.01183024,0.1481296,0.07666866,0.002809693,0.002317726,0.0007373678,0.0001200804,0.004410739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.749819,"threshold_uncertainty_score":0.9246604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007701581619329071,"score_gpt":0.2487512087754637,"score_spread":0.2410496271561346,"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."}}