{"id":"W3088083099","doi":"10.3390/rs12193141","title":"Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Flood myth; Remote sensing; Environmental science; Flooding (psychology); Lidar; Digital elevation model; Cloud computing; Cartography; Geography; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001310547,0.000138931,0.000174289,0.00003611625,0.00008799363,0.00003785598,0.0001353757,0.0000324916,0.000002237308],"category_scores_gemma":[0.00004858279,0.0001474793,0.00000588899,0.0002721216,0.00002827811,0.0002015859,0.0007522058,0.0001326931,0.000003486833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001276057,"about_ca_system_score_gemma":0.00002469599,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3267049,"about_ca_topic_score_gemma":0.5423259,"domain_scores_codex":[0.9987823,0.00006119401,0.0002075493,0.000498225,0.0001909405,0.0002598287],"domain_scores_gemma":[0.9995585,0.00005116621,0.0000705184,0.0002244987,0.000002149993,0.00009316704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000058155,0.00002378517,0.7828119,0.0001113433,0.00004578543,0.0003460678,0.002850072,0.00175164,0.06850296,4.089947e-7,0.0003543253,0.1431435],"study_design_scores_gemma":[0.0004958073,0.000006282514,0.6595662,0.0001176048,0.000009743263,0.000003980856,0.000390913,0.3387735,0.0001972919,0.00001420464,0.000265358,0.000159114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970955,0.0002858698,0.0002027893,0.0003722244,0.00004203899,0.0002331831,0.000004677056,0.0000172764,0.001746463],"genre_scores_gemma":[0.9865438,0.00006217846,0.01316913,0.0000867022,0.0000357268,2.191974e-8,0.00002677663,0.00001267889,0.00006294477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3370219,"threshold_uncertainty_score":0.6777786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05991555483453617,"score_gpt":0.2158236999379916,"score_spread":0.1559081451034555,"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."}}