{"id":"W2061840085","doi":"10.1007/s11269-009-9503-5","title":"Impacts of Accuracy and Resolution of Conventional and LiDAR Based DEMs on Parameters Used in Hydrologic Modeling","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of New Brunswick","funders":"","keywords":"Lidar; Digital elevation model; Watershed; Remote sensing; Environmental science; Hydrological modelling; Hydrology (agriculture); Elevation (ballistics); Hydrogeology; Geology; Geotechnical engineering","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":[0.0002384877,0.00007751139,0.0001240959,0.000037321,0.00003312563,0.00001213054,0.00006398633,0.00003621852,0.00001931086],"category_scores_gemma":[0.00000419044,0.00002924765,0.00003519274,0.00006892161,0.00003491039,0.00004021023,0.00001706708,0.00003955217,6.594469e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006764773,"about_ca_system_score_gemma":4.284235e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001527648,"about_ca_topic_score_gemma":0.00003097627,"domain_scores_codex":[0.9992988,0.00003882961,0.000203195,0.0001637889,0.000161286,0.0001341263],"domain_scores_gemma":[0.9998407,0.00003378288,0.00004499828,0.00003481815,0.000008549174,0.0000371239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002340666,0.001484877,0.4211991,0.000277719,0.00008971773,0.00004356111,0.002299633,0.03807766,0.4787469,0.001127851,0.00004267762,0.0542697],"study_design_scores_gemma":[0.001606927,0.001389561,0.9156439,0.0002146871,0.00003418278,7.927027e-7,0.000384771,0.06073631,0.01784574,0.001275742,0.0006537748,0.0002136433],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988475,0.00004664772,0.00001565908,0.0007300224,0.000009839689,0.0001741589,0.000003109173,0.00001120463,0.0001619033],"genre_scores_gemma":[0.9996011,0.00003739743,0.00009211033,0.000229218,0.000004969666,0.000003418486,0.00002096011,4.136326e-7,0.00001036668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4944448,"threshold_uncertainty_score":0.1192685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02681534362544805,"score_gpt":0.2214881874575142,"score_spread":0.1946728438320662,"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."}}