{"id":"W2811007541","doi":"10.1016/j.jag.2018.06.019","title":"Using FloodRisk GIS freeware for uncertainty analysis of direct economic flood damages in Italy","year":2018,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Cohesion Fund; Basilicata Regional authority; U.S. Army Corps of Engineers; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Damages; Flood myth; Context (archaeology); Flood risk assessment; Terrain; Estimation; Environmental science; Risk analysis (engineering); Geography; Computer science; Environmental resource management; Environmental planning; Cartography; Engineering; Business","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":[0.0003930586,0.00007782751,0.0001723818,0.0003143087,0.00004027619,0.00004584178,0.0001498339,0.00003480649,0.000193568],"category_scores_gemma":[0.00001791614,0.00007132244,0.00007073436,0.0002019514,0.00005112724,0.0006683012,0.00005427331,0.00004285966,0.00000519591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008948919,"about_ca_system_score_gemma":0.00001804425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003590099,"about_ca_topic_score_gemma":0.001898106,"domain_scores_codex":[0.9990367,0.00000932169,0.0005385102,0.00008222814,0.0002461259,0.0000871256],"domain_scores_gemma":[0.9992679,0.00003980872,0.0005173822,0.00006804265,0.00007621122,0.00003067264],"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":[0.0003587093,0.00008643114,0.127743,0.00001828325,0.0007136593,6.812534e-7,0.00221453,0.8239098,0.002034007,0.002177153,0.0004458014,0.04029793],"study_design_scores_gemma":[0.001050936,0.00009559333,0.4057134,0.00001798499,0.0001787501,9.007932e-7,0.0006959644,0.5865957,0.001459739,0.0005563222,0.003530613,0.0001040049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886216,0.000005487214,0.008297938,0.0001421538,0.0001837877,0.0001561456,0.00002657155,0.000004671972,0.00256163],"genre_scores_gemma":[0.989055,0.00004474983,0.01062068,0.0001204576,0.00006456544,0.000003357394,0.00006209693,0.000003441913,0.00002565247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2779704,"threshold_uncertainty_score":0.2908445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01992682984538352,"score_gpt":0.2772257941978508,"score_spread":0.2572989643524673,"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."}}