{"id":"W2089610733","doi":"10.1002/hyp.8195","title":"GIS‐evaluation of two slope‐calculation methods regarding their suitability in slope analysis using high‐precision LiDAR digital elevation models","year":2011,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Digital elevation model; Lidar; Watershed; Terrain; Elevation (ballistics); Remote sensing; Geology; Topographic Wetness Index; Photogrammetry; STREAMS; Erosion; Hydrology (agriculture); Environmental science; Geomorphology; Geometry; Geography; Cartography; Mathematics; Computer science; 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":[0.002437577,0.0001847988,0.0004024684,0.00007551596,0.0001091415,0.00004235511,0.0002208597,0.000167769,0.0005413793],"category_scores_gemma":[0.0005277526,0.00007374944,0.0001492566,0.001696267,0.00008759596,0.0006887165,0.00005137902,0.0001225075,0.000003002741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005629615,"about_ca_system_score_gemma":0.00002577441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002773856,"about_ca_topic_score_gemma":0.0002517792,"domain_scores_codex":[0.9977983,0.000352564,0.000600913,0.0004898942,0.0005166021,0.000241747],"domain_scores_gemma":[0.9987425,0.0004004911,0.0002346726,0.00009960566,0.0004522086,0.00007051099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005602317,0.001258484,0.5627401,0.00006131342,0.0001567936,0.000002591094,0.001349208,0.07984725,0.1067039,0.0006376122,0.000002736361,0.2466798],"study_design_scores_gemma":[0.0006134159,0.0003039067,0.6196508,0.00004283308,0.0003267701,0.000001436345,0.0002486262,0.3042054,0.02174237,0.05250842,0.00001754218,0.0003385402],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988342,0.0001392153,0.01001988,0.00004486644,0.00003892228,0.0003317088,0.00001706605,0.00005703551,0.001009343],"genre_scores_gemma":[0.9971474,0.00002483083,0.002589719,0.00003984753,0.00003160526,0.00002146255,0.000138566,0.000001436754,0.00000510238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2463412,"threshold_uncertainty_score":0.5927724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.171143094896831,"score_gpt":0.3350491370473705,"score_spread":0.1639060421505396,"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."}}