{"id":"W4400410305","doi":"10.1080/19475705.2024.2364777","title":"A low-cost IoT-based deep learning method of water gauge measurement for flood monitoring","year":2024,"lang":"en","type":"article","venue":"Geomatics Natural Hazards and Risk","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Internet of Things; Flood myth; Computer science; Gauge (firearms); Deep learning; Environmental science; Real-time computing; Remote sensing; Artificial intelligence; Embedded system; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0003017161,0.0005196438,0.0002965056,0.0005482268,0.000231758,0.0002999119,0.0007303916,0.0006390465,0.001420861],"category_scores_gemma":[0.0007023863,0.0002430235,0.0004285386,0.0005964286,0.0002327405,0.0006057136,0.0006033484,0.0006071705,0.0005303872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003859102,"about_ca_system_score_gemma":0.0005638236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002992325,"about_ca_topic_score_gemma":0.005551554,"domain_scores_codex":[0.9997799,0.00002278117,0.00001165048,0.00006451973,0.00008677642,0.00003433663],"domain_scores_gemma":[0.999845,0.00002788064,0.00002650063,0.00002883098,0.00006008662,0.00001164619],"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":[0.0001543109,0.0002385447,0.005529353,0.0001128733,0.00008003629,0.0001398536,0.0000702184,0.1044943,0.06422383,0.00354225,0.00396926,0.8174451],"study_design_scores_gemma":[0.000007345485,0.00006049678,0.002448154,0.00001243782,0.00001884814,0.00009647878,0.00001169527,0.9785066,0.0155391,0.00118718,0.002094796,0.00001684321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02957579,0.0001911593,0.9663919,0.0001346577,0.00009628738,0.00005089448,0.0001355457,0.001112578,0.002311111],"genre_scores_gemma":[0.5682096,0.0003213598,0.4251994,0.0002571847,0.00007910564,0.0001292766,0.0004969157,0.0001015918,0.005205492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002992325,"threshold_uncertainty_score":0.005949855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009916580981177054,"score_gpt":0.2650018557539725,"score_spread":0.2550852747727955,"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."}}