{"id":"W4417279049","doi":"10.5194/ica-abs-10-61-2025","title":"Enhancing Flood Management in Canada: Leveraging SAR and AI for Improved Emergency Response","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Emergency response; Flood myth; Disaster response; Emergency management; Global Positioning 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":[0.0008115879,0.0003666125,0.000215765,0.0005958891,0.001489132,0.002533119,0.0005756513,0.0005631998,0.004602637],"category_scores_gemma":[0.002314239,0.0001383294,0.0002582518,0.0007758706,0.0005624426,0.000760321,0.0008830833,0.001079878,0.0004611634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01188433,"about_ca_system_score_gemma":0.05265298,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.951889,"about_ca_topic_score_gemma":0.9816762,"domain_scores_codex":[0.9994646,0.00007998521,0.00001407838,0.00003792229,0.000200768,0.0002027131],"domain_scores_gemma":[0.9984779,0.0001760421,0.00008848005,0.00004246182,0.000938891,0.0002763039],"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":[0.0005427942,0.0007769625,0.1361929,0.0004962251,0.0002338827,0.0005847496,0.001379201,0.07719474,0.02347299,0.007919068,0.1148844,0.636322],"study_design_scores_gemma":[0.0002311983,0.0004250133,0.4950579,0.0003327376,0.0004409479,0.0002434096,0.01015092,0.2399596,0.01458255,0.008352839,0.229951,0.000271905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7076979,0.003543803,0.03311769,0.05339959,0.0006350714,0.0004553502,0.004693836,0.002256044,0.1942006],"genre_scores_gemma":[0.9656191,0.001598733,0.01496029,0.0009757587,0.00007611274,0.00003313847,0.0006780201,0.0000741468,0.01598476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04811102,"threshold_uncertainty_score":0.09678864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004872487395993393,"score_gpt":0.2290595474208123,"score_spread":0.2241870600248189,"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."}}