{"id":"W2790688155","doi":"10.1002/met.1713","title":"Modelling weather risk preferences with multi‐criteria decision analysis for an aerospace vehicle launch","year":2018,"lang":"en","type":"article","venue":"Meteorological Applications","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Tecnológico de Aeronáutica","keywords":"Computer science; Operations research; Decision support system; Consensus forecast; Decision analysis; Probabilistic logic; Econometrics; Economics; Engineering; Data mining","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":[],"consensus_categories":[],"category_scores_codex":[0.002596105,0.0002203113,0.0005537292,0.0003148716,0.0008670894,0.0002992493,0.001068165,0.0001742711,0.0007454476],"category_scores_gemma":[0.0003915417,0.0001236402,0.0003126221,0.002988787,0.0003556677,0.0002886643,0.00009869571,0.0001491007,0.0002570693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002155516,"about_ca_system_score_gemma":0.00002665912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002429507,"about_ca_topic_score_gemma":0.001676442,"domain_scores_codex":[0.9968227,0.0003098885,0.0006397282,0.001104225,0.0007616235,0.0003618022],"domain_scores_gemma":[0.9955493,0.001829169,0.0003401443,0.001211657,0.0008352368,0.0002345039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001283593,0.001471601,0.1914946,0.000003928542,0.001164263,9.770575e-7,0.001402253,0.3362133,0.001609955,0.006583198,0.0008086337,0.4579637],"study_design_scores_gemma":[0.0003982977,0.0005238011,0.02010553,0.000001585052,0.0005859006,5.552835e-7,0.0003093507,0.8306842,0.0001829225,0.131868,0.01510921,0.0002305894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3638208,0.00005852837,0.6350341,0.0002452965,0.00001307909,0.0003610367,0.00008737194,0.00005755771,0.0003221372],"genre_scores_gemma":[0.7139253,0.00006174632,0.285048,0.00008312994,0.00009180517,0.0003952192,0.00002330025,0.000008834631,0.0003626843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.494471,"threshold_uncertainty_score":0.8162128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1681912547679536,"score_gpt":0.4021588714709599,"score_spread":0.2339676167030063,"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."}}