{"id":"W4404733155","doi":"10.1016/j.biocon.2024.110847","title":"Managing multiple threats: Evaluating the efficacy of broad-scale introduced predator management in improving native mammal resilience to fire","year":2024,"lang":"en","type":"article","venue":"Biological Conservation","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Department of Environment and Conservation","funders":"Australian Research Council","keywords":"Predator; Resilience (materials science); Mammal; Marine mammal; Scale (ratio); Geography; Environmental resource management; Ecology; Environmental science; Biology; Predation; Cartography","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":[0.001033901,0.0001133788,0.0001196295,0.00004221362,0.0001288513,0.00002846903,0.0002448958,0.00007726865,0.0001459305],"category_scores_gemma":[0.0005231353,0.00007725877,0.00003641287,0.0005554502,0.0001522905,0.0001722463,0.0002584867,0.0001393847,0.00006514094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001459797,"about_ca_system_score_gemma":0.0000145583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002838733,"about_ca_topic_score_gemma":0.0003402097,"domain_scores_codex":[0.9986497,0.0002123235,0.0003182825,0.0004319114,0.0001804801,0.0002073384],"domain_scores_gemma":[0.9990329,0.0006503991,0.00007366022,0.0001964117,0.00001571955,0.00003090223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002655704,0.00007540829,0.7946895,0.00002864609,0.00001660472,0.000006242205,0.0007773531,0.002644528,0.01924555,0.0006204507,0.0004229446,0.1812072],"study_design_scores_gemma":[0.0002351223,0.0002025981,0.9199978,0.00004483877,0.00001057921,0.000001173105,0.0002583473,0.07730495,0.0006346175,0.001073954,0.0001391816,0.0000969016],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992247,0.00007221938,0.001159574,0.005073198,0.0001415106,0.0007729223,0.00000368195,0.00005382251,0.0004760681],"genre_scores_gemma":[0.9964453,0.00001722789,0.002269507,0.0008656874,0.00003505233,0.0001300383,0.00001308892,0.000005369719,0.0002187138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1811103,"threshold_uncertainty_score":0.3150521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03735855866775432,"score_gpt":0.2972435973616658,"score_spread":0.2598850386939115,"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."}}