{"id":"W4394100075","doi":"10.6084/m9.figshare.4264265","title":"Multi-Criteria Decision Analysis for Recreational Trout Fisheries in British Columbia, Canada: A Bayesian Network Implementation","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Trout; Fishery; Recreational fishing; Recreation; Bayesian network; Bayesian probability; Fish <Actinopterygii>; Environmental science; Geography; Operations research; Computer science; Ecology; Engineering; Biology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001553058,0.0002016346,0.0004474209,0.0001062996,0.0002023707,0.001105435,0.001000604,0.0002164453,0.03250383],"category_scores_gemma":[0.0003630301,0.0002893854,0.0001469852,0.0006270972,0.00000624315,0.0003375375,0.0002368485,0.0001620349,0.00001933558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003109617,"about_ca_system_score_gemma":0.001263591,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6393028,"about_ca_topic_score_gemma":0.9980394,"domain_scores_codex":[0.9977753,0.00008828069,0.0005541119,0.0007308116,0.0003836598,0.0004678595],"domain_scores_gemma":[0.9983956,0.0003627035,0.0002783854,0.0005656157,0.0002646389,0.0001330506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000302337,0.00001511553,0.0002858684,0.00004836404,0.00006846705,0.00001986566,0.000006566792,0.00005775167,1.262194e-7,4.498201e-7,0.985196,0.01429843],"study_design_scores_gemma":[0.0006196172,0.00004559439,0.01835699,0.001977449,0.00006619332,0.0000068778,0.000008465955,0.01827783,5.400502e-7,0.0002564032,0.9598516,0.0005325028],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000007156988,0.0001262939,0.0540901,0.00006987477,0.0001854087,0.0004006162,0.9450866,0.00003064431,0.000003315433],"genre_scores_gemma":[0.0003130326,0.00001851114,0.01327987,0.0003104377,0.0002057022,0.0007349508,0.9850213,0.00001320433,0.0001030085],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3587366,"threshold_uncertainty_score":0.9999558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04408961251477926,"score_gpt":0.3027501360519743,"score_spread":0.258660523537195,"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."}}