{"id":"W3037786197","doi":"10.3390/w12061834","title":"Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Flood myth; Scale (ratio); Vulnerability (computing); Computer science; Flood risk assessment; Environmental resource management; Hazard; Environmental science; Data mining; Cartography; Remote sensing; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007007592,0.0006178389,0.0005451288,0.001530241,0.0004460897,0.0009039035,0.0006438696,0.0005082059,0.001156928],"category_scores_gemma":[0.002162768,0.0002582784,0.00061907,0.002075,0.0003667044,0.0006992229,0.0008792832,0.0003099984,0.0001405728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000973952,"about_ca_system_score_gemma":0.0007288102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02760329,"about_ca_topic_score_gemma":0.02574189,"domain_scores_codex":[0.9996791,0.0001391499,0.00002651382,0.00006791365,0.00003995523,0.00004748735],"domain_scores_gemma":[0.9993889,0.0002868973,0.000076697,0.00009685973,0.0001084456,0.00004217746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003612082,0.0003468614,0.1313485,0.0005525529,0.0002031612,0.004303427,0.001175811,0.6228611,0.007318894,0.005532286,0.003326456,0.2226697],"study_design_scores_gemma":[0.00005166676,0.0001238049,0.08845159,0.00009411517,0.00006792069,0.0004917926,0.00139134,0.8981297,0.003831825,0.002380245,0.004939369,0.00004671385],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485086,0.0004957592,0.04398603,0.0004452027,0.00003524732,0.0001783306,0.001141282,0.0004771481,0.004732311],"genre_scores_gemma":[0.9653524,0.0002625699,0.03322987,0.00001835443,0.000009296241,0.00006517998,0.0003504279,0.00004074901,0.0006710964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02760329,"threshold_uncertainty_score":0.05488527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01938687442380856,"score_gpt":0.2404085265759529,"score_spread":0.2210216521521444,"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."}}