{"id":"W1857089916","doi":"10.1139/cjfr-2015-0148","title":"Global sensitivity analysis for the Rothermel model based on high-dimensional model representation","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Environmental science; Variance-based sensitivity analysis; Parametric model; Parametric statistics; Variance (accounting); Terrain; Meteorology; Mathematics; Statistics; Engineering; Geography; One-way analysis of variance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004210613,0.001648692,0.001181537,0.001316612,0.0005379069,0.001522874,0.001035283,0.001433866,0.003143259],"category_scores_gemma":[0.009483917,0.0005375978,0.002755526,0.0005891212,0.0009231177,0.001325242,0.001515592,0.001922962,0.000181239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001712181,"about_ca_system_score_gemma":0.001082959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.016473,"about_ca_topic_score_gemma":0.006240658,"domain_scores_codex":[0.9985988,0.0006722658,0.00005630971,0.0002905762,0.000211512,0.0001705369],"domain_scores_gemma":[0.9939551,0.00485418,0.000387388,0.0002487888,0.0004679945,0.00008660695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001849881,0.00001033604,0.0008285962,0.00001730578,0.0000324806,0.00004412326,0.00002210583,0.99515,0.0004222755,0.002292127,0.00009070755,0.001071351],"study_design_scores_gemma":[0.000002409751,0.00001826866,0.0003509806,0.000003739389,0.00001148056,0.000008887601,0.00001674457,0.997739,0.000174649,0.001589879,0.00007572458,0.00000828636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.343745,0.0004343857,0.6464119,0.0005689685,0.00006238568,0.0001594387,0.0006958329,0.0005214761,0.007400673],"genre_scores_gemma":[0.9828439,0.0001040597,0.01507275,0.00006232452,0.00001072244,0.0001203124,0.0003463221,0.00006639036,0.001373159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.016473,"threshold_uncertainty_score":0.03275424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2921601362052443,"score_gpt":0.4227481578946782,"score_spread":0.1305880216894339,"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."}}