{"id":"W2078701858","doi":"10.1016/j.proenv.2010.10.063","title":"Uncertainty propagation in environmental decision making using random sets","year":2010,"lang":"en","type":"article","venue":"Procedia Environmental Sciences","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Uncertainty quantification; Probability density function; Set (abstract data type); Propagation of uncertainty; Mathematics; Interpretation (philosophy); Uncertainty theory; Random variable; Computer science; Probability distribution; Possibility theory; Mathematical optimization; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006446017,0.0003712391,0.0004431779,0.0007112963,0.0006902585,0.0006482833,0.001620062,0.0001916246,0.00282508],"category_scores_gemma":[0.002069151,0.0002794017,0.000148502,0.0009322887,0.001102775,0.001597478,0.0006829107,0.0004598612,0.0005093709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002643189,"about_ca_system_score_gemma":0.00008764127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002497006,"about_ca_topic_score_gemma":0.0001481429,"domain_scores_codex":[0.9926232,0.0002245245,0.001255227,0.001478677,0.003690859,0.000727563],"domain_scores_gemma":[0.9969331,0.001750227,0.0005173954,0.0005826897,0.00001234975,0.0002042184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002251377,0.0002983501,0.3737778,0.000003859121,0.000004585713,0.00003252954,0.001466175,0.01202028,0.2759622,0.00007900749,0.00008489101,0.3360452],"study_design_scores_gemma":[0.002872948,0.0001585125,0.3150492,0.0001178039,0.00001755828,0.0002075773,0.003797979,0.6411987,0.005177713,0.0277495,0.00267702,0.0009754733],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925467,0.0001104491,0.004901505,0.00009828701,0.001034925,0.0006951506,0.00003492555,0.00003614032,0.0005418864],"genre_scores_gemma":[0.9728315,0.00001962493,0.02674837,0.0001787031,0.0001226793,0.00003292304,0.000005076547,0.00002227945,0.0000388118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6291784,"threshold_uncertainty_score":0.9999658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07350891885830818,"score_gpt":0.3825496857268719,"score_spread":0.3090407668685637,"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."}}