{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002940889,0.0001000957,0.00009206051,0.00001644459,0.0000890559,0.00002919082,0.0004648143,0.00003292082,0.0003396816],"category_scores_gemma":[0.00000305545,0.00007903354,0.00001840118,0.0000586105,0.00002506711,0.0004793426,0.0006858511,0.0000643548,0.0006781262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004599819,"about_ca_system_score_gemma":0.000001083399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001894856,"about_ca_topic_score_gemma":0.0005788882,"domain_scores_codex":[0.9988874,0.0000341728,0.0001558096,0.0004864725,0.0001599054,0.0002762299],"domain_scores_gemma":[0.9994295,0.000005013322,0.00003197497,0.0004589972,6.570742e-7,0.0000738516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007496364,0.0008430363,0.7383717,0.00006951408,0.00005565777,0.000007111576,0.01156397,0.002999417,0.2064221,0.0001311244,0.01285174,0.02660966],"study_design_scores_gemma":[0.001454276,0.0000834185,0.1729627,0.000005070068,0.00004084786,2.82946e-7,0.0009022564,0.1522499,0.005549006,0.000475959,0.6659575,0.0003188618],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8979716,0.00001090532,0.09846717,0.001341847,0.00004213559,0.0008885593,0.00007245858,0.00004096228,0.001164348],"genre_scores_gemma":[0.9923168,0.00002347587,0.006202946,0.0005143933,0.00006536488,0.00009906339,0.0004667398,0.00001709131,0.0002941228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6531057,"threshold_uncertainty_score":0.8716166,"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."}}