{"id":"W2046385589","doi":"10.1890/14-0180.1","title":"Evidence‐based tool surpasses expert opinion in predicting probability of eradication of aquatic nonindigenous species","year":2015,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Fisheries and Oceans Canada; University of Prince Edward Island","funders":"Fisheries and Oceans Canada; Killam Trusts; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Expert elicitation; Expert opinion; Computer science; Expert system; Process (computing); Scientific evidence; Subject-matter expert; Machine learning; Management science; Ecology; Artificial intelligence; Statistics; Engineering; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.1868546,0.00248353,0.003473136,0.01397699,0.0008974099,0.00672396,0.002486998,0.005611061,0.002609765],"category_scores_gemma":[0.4780565,0.001107582,0.006149363,0.007471283,0.001794203,0.007322107,0.003515321,0.002535507,0.0004401963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002518203,"about_ca_system_score_gemma":0.002951855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003066708,"about_ca_topic_score_gemma":0.004278103,"domain_scores_codex":[0.7710934,0.190694,0.01390304,0.007238172,0.0158541,0.001217331],"domain_scores_gemma":[0.2554873,0.7072634,0.019206,0.007836609,0.009111637,0.001095163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005227859,0.0008346311,0.2574898,0.02232809,0.03146839,0.001636132,0.002978962,0.2882642,0.001795629,0.01541964,0.005025373,0.3675313],"study_design_scores_gemma":[0.001019253,0.006289446,0.1346298,0.01703382,0.02595814,0.002303731,0.002568963,0.6813958,0.004474664,0.1108942,0.01238922,0.00104288],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4643909,0.06815945,0.4282201,0.009552681,0.000643433,0.0020133,0.002167038,0.000734484,0.02411857],"genre_scores_gemma":[0.9332008,0.002986186,0.06239466,0.000557897,0.0001024399,0.0003002742,0.0002534639,0.00002932679,0.0001750285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1868546,"threshold_uncertainty_score":0.9881932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1809757010473805,"score_gpt":0.3224007528546666,"score_spread":0.1414250518072861,"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."}}