{"id":"W2948007733","doi":"10.3390/ijgi8060250","title":"A GIS Tool for Mapping Dam-Break Flood Hazards in Italy","year":2019,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Ministero dello Sviluppo Economico","keywords":"Flood myth; Hydrograph; Dam failure; Digital elevation model; Geospatial analysis; Computer science; Geographic information system; Environmental science; Dam break; Geomatics; Flood mitigation; Civil engineering; Hydrology (agriculture); Geology; Geotechnical engineering; Engineering; Geography; Remote sensing","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":[0.0006012354,0.00009840301,0.0001314839,0.0002595372,0.00003210514,0.0001345875,0.0004015742,0.00004402486,0.0006466664],"category_scores_gemma":[0.00006156118,0.00008756309,0.0001056437,0.0001142393,0.00001786467,0.003816401,0.0001264799,0.0001131061,0.0003896073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000347892,"about_ca_system_score_gemma":0.0000293565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001031663,"about_ca_topic_score_gemma":0.00002791668,"domain_scores_codex":[0.9985069,0.00001505977,0.0006018206,0.00006785592,0.0006516543,0.0001567028],"domain_scores_gemma":[0.9993172,0.00003683947,0.0004298946,0.00009260327,0.0000873507,0.00003612614],"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":[0.0008788196,0.0004633167,0.309004,0.0001397323,0.0005291229,0.00002456639,0.007059617,0.07785104,0.003494334,0.008155066,0.0623577,0.5300426],"study_design_scores_gemma":[0.006669376,0.0004139135,0.3894366,0.0002240422,0.0000411582,0.00006739758,0.001881775,0.05379028,0.001041529,0.002835417,0.5431445,0.0004539978],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9357315,0.00001323603,0.04439319,0.001987147,0.002015672,0.0004938977,0.00002327905,0.00001435792,0.01532772],"genre_scores_gemma":[0.991843,0.00003594114,0.007035972,0.0006175392,0.00009132895,0.00001199195,0.00003772544,0.00000492762,0.0003216093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5295886,"threshold_uncertainty_score":0.7080544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004212507576924368,"score_gpt":0.234913729647116,"score_spread":0.2307012220701916,"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."}}