{"id":"W4327716813","doi":"10.1088/2515-7620/acc56d","title":"Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment","year":2023,"lang":"en","type":"article","venue":"Environmental Research Communications","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Global Water Futures; Canada First Research Excellence Fund; Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Lidar; Digital elevation model; Shuttle Radar Topography Mission; Elevation (ballistics); Terrain; Remote sensing; Vegetation (pathology); Environmental science; Tree canopy; Earth observation; Satellite imagery; Canopy; Physical geography; Satellite; Geology; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009125164,0.0004622644,0.0003525764,0.0007760471,0.0002696629,0.0006208512,0.0006740151,0.0006208198,0.0008100827],"category_scores_gemma":[0.001623075,0.0001352973,0.0004349751,0.0006790267,0.0004020059,0.0004549866,0.0004962317,0.0004036452,0.0003342573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006539377,"about_ca_system_score_gemma":0.0005738154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04861807,"about_ca_topic_score_gemma":0.04741477,"domain_scores_codex":[0.999742,0.00004896406,0.00002012972,0.0000691176,0.0000725065,0.00004727876],"domain_scores_gemma":[0.9994695,0.0001446413,0.00005587843,0.0001100572,0.0001583457,0.00006158085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005596841,0.0005237565,0.1342438,0.0001784685,0.0002097101,0.0009299549,0.0003046347,0.8135173,0.01531939,0.0007710346,0.003826389,0.02961575],"study_design_scores_gemma":[0.0001657005,0.0002333137,0.1754102,0.00006139519,0.00004682376,0.0001240632,0.0003646761,0.8133977,0.007232538,0.0002888716,0.002627442,0.00004721513],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933143,0.00005988129,0.001938824,0.00005358405,0.00001970303,0.00002856533,0.002745964,0.0003939108,0.001445369],"genre_scores_gemma":[0.9928346,0.00003105378,0.002741887,0.00001898332,0.000004015086,0.00001427811,0.004104558,0.00002785783,0.000222689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04861807,"threshold_uncertainty_score":0.09667015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09243398403197148,"score_gpt":0.3632075744718312,"score_spread":0.2707735904398597,"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."}}