{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001039805,0.0001782675,0.0002041808,0.00006476424,0.0001949826,0.00008154455,0.0001255714,0.0000604619,0.00007179315],"category_scores_gemma":[0.00004675518,0.0001131614,0.0001144332,0.0001105483,0.00004879546,0.00009138516,0.0001306551,0.0002009312,0.0000202206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001053864,"about_ca_system_score_gemma":0.00001114828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002519107,"about_ca_topic_score_gemma":0.0001138416,"domain_scores_codex":[0.9985824,0.00006841512,0.0002707123,0.0002859957,0.0004708533,0.0003216859],"domain_scores_gemma":[0.9996202,0.00008269212,0.00006327546,0.0001436212,0.00002326572,0.00006695687],"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":[0.00005214292,0.0001406294,0.007711018,0.0009986599,0.0002672272,0.000009896027,0.0015477,0.01998807,0.02121195,0.0002017678,0.0004994922,0.9473714],"study_design_scores_gemma":[0.001164066,0.0002367556,0.01008482,0.0003762245,0.0005191812,0.000002881647,0.0005310496,0.8752195,0.09493516,0.001725427,0.01472353,0.0004813795],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.840554,0.001405022,0.1552168,0.0002058963,0.001079785,0.001009586,0.00002308948,0.0001230188,0.0003827632],"genre_scores_gemma":[0.9285978,0.0002886509,0.07070167,0.00001140038,0.00008021056,0.00005836739,0.00001486743,0.00002192221,0.0002251294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9468901,"threshold_uncertainty_score":0.4614588,"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."}}