{"id":"W4308261138","doi":"10.1016/j.isci.2022.105512","title":"Insights into the quantification and reporting of model-related uncertainty across different disciplines","year":2022,"lang":"en","type":"review","venue":"iScience","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Oceanic and Atmospheric Administration; Sight Research UK; Réseau de cancérologie Rossy; Department for Business, Energy and Industrial Strategy, UK Government; Natural Environment Research Council; Met Office; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Toolbox; CLARITY; Consistency (knowledge bases); Data science; Audit; Management science; Computer science; Best practice; Uncertainty quantification; Field (mathematics); Risk analysis (engineering); Political science; Engineering; Business; Accounting; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002125673,0.0003064018,0.0007644916,0.00004634965,0.0009813497,0.00005220927,0.0006922702,0.000101644,0.0001284566],"category_scores_gemma":[0.000697328,0.000180791,0.0001478899,0.0006473734,0.001215642,0.0002167843,0.001367689,0.0004763566,0.00002170874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003893045,"about_ca_system_score_gemma":0.00006620141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003407645,"about_ca_topic_score_gemma":0.0002196482,"domain_scores_codex":[0.9958836,0.0003192968,0.001675933,0.001037342,0.0007185598,0.0003652629],"domain_scores_gemma":[0.9960152,0.0003704464,0.002738749,0.0007594331,0.000006721694,0.0001094408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[9.760487e-7,0.00004238799,0.0002845401,0.0005143113,0.000007912391,0.00000329895,0.005503302,0.003386951,0.0001186189,0.0001062205,0.000007632133,0.9900239],"study_design_scores_gemma":[0.0003023932,0.0001540331,0.01166669,0.003721505,0.0004167274,0.0000706634,0.003653344,0.1383927,0.00008894667,0.005498094,0.8343922,0.001642739],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1288378,0.8673653,0.001234708,0.0001511878,0.0002052103,0.001319873,0.00001574555,0.00003562909,0.0008345923],"genre_scores_gemma":[0.1268729,0.8725584,0.0001095145,0.00003233945,0.00001127334,0.0001593329,0.0000233343,0.00002239711,0.0002105681],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9883811,"threshold_uncertainty_score":0.7547848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1247833709285167,"score_gpt":0.3910951667740726,"score_spread":0.2663117958455559,"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."}}