{"id":"W3108784197","doi":"10.3390/geosciences10120478","title":"Prioritizing Flood-Prone Areas Using Spatial Data in the Province of New Brunswick, Canada","year":2020,"lang":"en","type":"article","venue":"Geosciences","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of New Brunswick; Université de Moncton; University of Winnipeg","funders":"Public Safety Canada; Université de Moncton","keywords":"Flood myth; Prioritization; Flood risk assessment; Scale (ratio); Hazard; Environmental resource management; Environmental planning; Geography; Risk analysis (engineering); Environmental science; Cartography; Engineering; Business","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004045795,0.0000831986,0.0001001454,0.00001297088,0.0001047164,0.00004380883,0.001205939,0.00001341185,0.00007782227],"category_scores_gemma":[0.00005397358,0.00005777723,0.00001095389,0.0003772437,0.0001371493,0.0003429768,0.0006418671,0.00007282114,0.000004917546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005467386,"about_ca_system_score_gemma":0.0008945863,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9660559,"about_ca_topic_score_gemma":0.9824571,"domain_scores_codex":[0.9985772,0.00004488372,0.0001774734,0.0003376431,0.000629081,0.0002336684],"domain_scores_gemma":[0.999513,0.0000268828,0.00008347817,0.0003071806,0.000002321635,0.00006708212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002207979,0.0001227817,0.9104305,0.00005805428,0.00001128345,0.0001143019,0.00264571,0.00613998,0.003676898,0.0005400511,0.0190387,0.05719968],"study_design_scores_gemma":[0.0003631369,0.0001075644,0.8469636,0.00002939066,0.0000250396,0.00000316326,0.002522122,0.1077837,0.0003611567,0.0000896385,0.04149333,0.000258156],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891573,0.000112322,0.003529762,0.003893011,0.0001800071,0.0003946718,0.000008598866,0.0000106626,0.002713647],"genre_scores_gemma":[0.9958866,0.00001705851,0.003329157,0.0005860113,0.00006245029,6.495907e-7,0.000004826417,0.000003036368,0.00011026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1016437,"threshold_uncertainty_score":0.2356087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03595145468113068,"score_gpt":0.2575584106800335,"score_spread":0.2216069559989028,"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."}}