{"id":"W2417821778","doi":"10.5194/isprs-annals-iii-8-27-2016","title":"RAPID RISK EVALUATION (ER<sup>2</sup>) USING MS EXCEL SPREADSHEET: A CASE STUDY OF FREDERICTON (NEW BRUNSWICK, CANADA)","year":2016,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; University of New Brunswick","funders":"Public Safety Canada","keywords":"Flood myth; Damages; Computer science; Hazard; Software; Vulnerability (computing); Computer security; Geography; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001551832,0.0007400693,0.0002615768,0.001105352,0.002037119,0.001567683,0.001565572,0.0006067334,0.006794278],"category_scores_gemma":[0.003329924,0.000346362,0.0002815792,0.002082968,0.0007462394,0.0006350009,0.0005713503,0.0004306536,0.0009939588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02114729,"about_ca_system_score_gemma":0.02167245,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9488745,"about_ca_topic_score_gemma":0.9712251,"domain_scores_codex":[0.999279,0.0001674472,0.00003436574,0.00006771951,0.0003153757,0.000136294],"domain_scores_gemma":[0.9976513,0.0008114235,0.00007460106,0.0001217347,0.001138243,0.0002026783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002095906,0.001637424,0.189864,0.0008247771,0.0001371573,0.02541464,0.01951106,0.1731414,0.01738034,0.01812225,0.06210821,0.4897628],"study_design_scores_gemma":[0.0004043941,0.001921144,0.2852955,0.0005180375,0.0002585692,0.002734173,0.05847843,0.350802,0.03924933,0.004647384,0.255189,0.0005020259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093065,0.0003645647,0.01586268,0.001009266,0.00004041947,0.001178162,0.002958962,0.001094269,0.06818511],"genre_scores_gemma":[0.9016717,0.0006604223,0.03210127,0.0001487711,0.000007160455,0.000228467,0.002321718,0.000281031,0.06257943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05112547,"threshold_uncertainty_score":0.1534351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05124591308475652,"score_gpt":0.2990640253111742,"score_spread":0.2478181122264176,"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."}}