{"id":"W1668900779","doi":"10.1002/esp.3425","title":"Improvement of streams hydro‐geomorphological assessment using LiDAR DEMs","year":2013,"lang":"en","type":"article","venue":"Earth Surface Processes and Landforms","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Concordia University","funders":"","keywords":"Lidar; Digital elevation model; Stream power; Elevation (ballistics); Channel (broadcasting); Remote sensing; STREAMS; Hydrology (agriculture); Environmental science; Ranging; River morphology; Geology; Geomorphology; Erosion; Geodesy; Sediment","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":[0.0009285083,0.0004871738,0.0002243503,0.00253562,0.0002051113,0.0009040741,0.0004445314,0.0001971595,0.001818218],"category_scores_gemma":[0.003230761,0.0002113043,0.0002287907,0.001434521,0.0001011402,0.0005816724,0.0003528649,0.0002327245,0.0008050005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000839276,"about_ca_system_score_gemma":0.0007125041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06933288,"about_ca_topic_score_gemma":0.1012965,"domain_scores_codex":[0.9997047,0.00008964737,0.00002411872,0.00005627399,0.0001017163,0.00002358019],"domain_scores_gemma":[0.9983658,0.000260077,0.0001170115,0.0001686704,0.001052558,0.00003586689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001759634,0.0001473306,0.1233422,0.0002200428,0.000104497,0.0003009326,0.0004310069,0.1337862,0.03385294,0.001591607,0.007667996,0.6983792],"study_design_scores_gemma":[0.00003067728,0.00003839764,0.1353658,0.00007175822,0.00003609169,0.0000820799,0.0003328726,0.8381526,0.01432156,0.0005829552,0.01093987,0.00004539939],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5356966,0.0003137437,0.427393,0.0003155648,0.00004444834,0.0005405557,0.01282766,0.01199012,0.01087831],"genre_scores_gemma":[0.7301736,0.0001709312,0.2626342,0.00003917184,0.00001049688,0.0001315149,0.004956845,0.0001382122,0.001745077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06933288,"threshold_uncertainty_score":0.1378586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009905239129271578,"score_gpt":0.2268082510046445,"score_spread":0.2169030118753729,"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."}}