{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004367886,0.00009438745,0.0001119664,0.0005721319,0.00008787469,0.0001530227,0.0002615153,0.0000319853,0.0000960556],"category_scores_gemma":[0.00002601488,0.00007359887,0.00001515868,0.0002672608,0.0001121532,0.003134296,0.0001982897,0.0001060549,0.00003543171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002150841,"about_ca_system_score_gemma":0.0000111002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000952767,"about_ca_topic_score_gemma":0.004082724,"domain_scores_codex":[0.9988915,0.00002785483,0.0003564906,0.00008229467,0.0004861196,0.0001556838],"domain_scores_gemma":[0.9994577,0.0000108198,0.0002507932,0.0001002666,0.0001118931,0.00006852724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001875148,0.0001207718,0.9561004,0.000003896997,0.00002867741,0.0002038692,0.007753343,0.0000735095,0.00001892258,0.0004845296,0.0003587592,0.0346658],"study_design_scores_gemma":[0.002203013,0.00413554,0.9205275,0.00008762194,0.00004510974,0.001114137,0.06034406,0.001834777,0.0002308589,0.0002014699,0.009005234,0.0002706819],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960762,0.000005708805,0.001155893,0.0009527738,0.0002569854,0.0002677366,0.000006555737,0.00001200381,0.001266192],"genre_scores_gemma":[0.9973134,0.00001626504,0.002176116,0.0003755423,0.00008132174,0.00001817288,0.000002657807,0.000003496428,0.00001298241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05259071,"threshold_uncertainty_score":0.3001275,"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."}}