{"id":"W2519233151","doi":"10.1371/journal.pone.0162932","title":"Supporting Risk Assessment: Accounting for Indirect Risk to Ecosystem Components","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; North Pacific Marine Science Organization; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; David and Lucile Packard Foundation","keywords":"Risk assessment; Ecosystem; Trophic level; Habitat; Environmental resource management; Predation; Ecosystem services; Ecology; Risk management; Geography; Risk analysis (engineering); Business; Biology; Environmental science; Computer 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":[],"consensus_categories":[],"category_scores_codex":[0.01235514,0.002621449,0.000811039,0.005059607,0.001103192,0.005186439,0.002395367,0.00159788,0.005631664],"category_scores_gemma":[0.044003,0.0005933673,0.001758415,0.003478495,0.00190593,0.005511339,0.004679388,0.002602371,0.0005672246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002882783,"about_ca_system_score_gemma":0.005027064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03329382,"about_ca_topic_score_gemma":0.03822086,"domain_scores_codex":[0.9930773,0.003107849,0.0004979951,0.0005654793,0.00237528,0.0003761619],"domain_scores_gemma":[0.9721091,0.01437874,0.004233167,0.003025837,0.005417959,0.0008351975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001440497,0.0001572919,0.1012731,0.0006879321,0.001217972,0.0005707745,0.001643858,0.3857745,0.001216013,0.1616701,0.006254612,0.3393897],"study_design_scores_gemma":[0.00002868566,0.0002550612,0.03345551,0.001027378,0.0008081329,0.0006801405,0.001222012,0.5088223,0.001477589,0.4268341,0.0251594,0.0002296791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1117223,0.003623626,0.8230023,0.004551696,0.0002761384,0.0005603683,0.001700392,0.0006433158,0.0539198],"genre_scores_gemma":[0.7691272,0.00241982,0.2203069,0.0002697564,0.0002678764,0.0003261483,0.0009775175,0.0001381887,0.006166481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03329382,"threshold_uncertainty_score":0.06620002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04474381459006596,"score_gpt":0.2686316837340653,"score_spread":0.2238878691439994,"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."}}