{"id":"W2807660536","doi":"10.1371/journal.pone.0198298","title":"A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the Productivity Susceptibility Analysis (PSA)","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Marine Fisheries Service","keywords":"Productivity; Computer science; Risk analysis (engineering); Range (aeronautics); System dynamics; Measure (data warehouse); Risk assessment; Population; Environmental resource management; Econometrics; Data mining; Environmental science; Engineering; Economics; Business","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":[0.08340588,0.001525045,0.0007292055,0.004143085,0.001277591,0.004744418,0.002143287,0.001222667,0.003842427],"category_scores_gemma":[0.1924526,0.0004279404,0.001548142,0.001745447,0.004602277,0.00530779,0.003584733,0.001904573,0.0003666446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006481661,"about_ca_system_score_gemma":0.004746943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007263129,"about_ca_topic_score_gemma":0.004481585,"domain_scores_codex":[0.9571237,0.02962257,0.001236347,0.001451473,0.009860769,0.0007050565],"domain_scores_gemma":[0.7447614,0.2082049,0.01070786,0.008901278,0.02617533,0.001249242],"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":[0.001707588,0.0008321935,0.05574898,0.002188989,0.0006174449,0.0004729942,0.01284901,0.3831765,0.007736064,0.3184234,0.004538809,0.2117081],"study_design_scores_gemma":[0.0001646295,0.001576697,0.01548805,0.000952109,0.0001808329,0.000224498,0.005824465,0.8115852,0.00547399,0.1527961,0.005463997,0.0002694282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2764373,0.0003153116,0.6866044,0.002876315,0.0001315966,0.001139228,0.0008714443,0.0004351541,0.0311893],"genre_scores_gemma":[0.8519981,0.0001341287,0.1460902,0.0002342055,0.00002287468,0.0005985999,0.0002028759,0.00004380626,0.0006752433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08340588,"threshold_uncertainty_score":0.4410977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2531543091419019,"score_gpt":0.4170180637519817,"score_spread":0.1638637546100798,"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."}}