{"id":"W3207304724","doi":"10.1109/igarss47720.2021.9554665","title":"Canada's Emergency Geomatics Service Near Real-Time Flood Mapping from Multi-Source Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Geomatics; Flood myth; Remote sensing; Digital elevation model; Floodplain; Lidar; Geographic information system; Vegetation (pathology); Geocoding; Service (business); Computer science; Geography; Environmental science; Cartography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004939326,0.0005509713,0.0002350466,0.002480863,0.001056088,0.001597089,0.0006905658,0.0003453933,0.006462773],"category_scores_gemma":[0.001564002,0.000221901,0.0002869274,0.003621225,0.000241708,0.0006721895,0.0008849183,0.0004685181,0.001412163],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005587148,"about_ca_system_score_gemma":0.01146188,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9241638,"about_ca_topic_score_gemma":0.9598598,"domain_scores_codex":[0.9994829,0.00003582814,0.00001704066,0.00005360621,0.0003137692,0.0000968936],"domain_scores_gemma":[0.9988061,0.00009110316,0.00005417523,0.0001129772,0.0008273063,0.0001083189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002954817,0.0001392397,0.070008,0.0005253013,0.0001561929,0.0005275007,0.001527564,0.02887683,0.013284,0.0052125,0.3274966,0.5519508],"study_design_scores_gemma":[0.0001113548,0.00005250342,0.3114658,0.0004085238,0.0000862349,0.0002563036,0.003726755,0.193508,0.01257927,0.004337642,0.4731701,0.000297573],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.269425,0.00214216,0.1593505,0.006700504,0.0004926337,0.001263801,0.3508463,0.02527683,0.1845022],"genre_scores_gemma":[0.6292835,0.002438626,0.1911434,0.0005536456,0.00009447221,0.0005194763,0.1304768,0.001122135,0.04436788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9944128,"threshold_uncertainty_score":0.1525655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02543939943638596,"score_gpt":0.2374539961117844,"score_spread":0.2120145966753985,"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."}}