{"id":"W2802044904","doi":"10.3390/ijgi7050161","title":"LiDAR—A Technology to Assist with Smart Cities and Climate Change Resilience: A Case Study in an Urban Metropolis","year":2018,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Lidar; Digital elevation model; Terrain; Catchment area; Environmental science; Resilience (materials science); Geographic information system; Climate change; Geography; Elevation (ballistics); Meteorology; Storm; Drainage basin; Remote sensing; Hydrology (agriculture); Physical geography; Cartography; Geology; Engineering","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":[0.0008555414,0.0004201208,0.0002180021,0.0008728473,0.001572188,0.001324694,0.0009078139,0.001151919,0.000954037],"category_scores_gemma":[0.001751904,0.0002684104,0.0003522173,0.001974662,0.001100859,0.00105006,0.001169251,0.0004748088,0.0001815484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002270887,"about_ca_system_score_gemma":0.001219835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08937696,"about_ca_topic_score_gemma":0.2197353,"domain_scores_codex":[0.9993566,0.0003108869,0.00002898803,0.0000571212,0.0001487088,0.00009781735],"domain_scores_gemma":[0.9989084,0.0005046619,0.0001002309,0.0001268627,0.0002164214,0.0001433837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005031044,0.001959268,0.5781931,0.0005667034,0.0002482818,0.05245797,0.02059049,0.1850352,0.01559728,0.01241599,0.01147408,0.1209586],"study_design_scores_gemma":[0.0001886543,0.001442877,0.3319444,0.0002201894,0.0002553365,0.007496733,0.1015899,0.4704015,0.022206,0.00485415,0.05911018,0.0002900729],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904734,0.00009929266,0.004281316,0.0007037826,0.00001222233,0.0001325407,0.000303256,0.00007922357,0.003914815],"genre_scores_gemma":[0.9890121,0.0001841114,0.009386992,0.00006717572,0.0000132167,0.00003963897,0.0001573732,0.00001824261,0.001121223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08937696,"threshold_uncertainty_score":0.1777135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009265619861168754,"score_gpt":0.2979144761527452,"score_spread":0.2886488562915764,"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."}}