Improvement of streams hydro‐geomorphological assessment using LiDAR DEMs
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Bibliographic record
Abstract
ABSTRACT Hydro‐geomorphological assessments are an essential component for riverine management plans. They usually require costly and time‐consuming field surveys to characterize the spatial variability of key variables such as flow depth, width, discharge, water surface slope, grain size and unit stream power throughout the river corridor. The objective of this research is to develop automated tools for hydro‐geomorphological assessments using high‐resolution LiDAR digital elevation models (DEMs). More specifically, this paper aims at developing geographic information system (GIS) tools to extract channel slope, width and discharge from 1 m‐resolution LiDAR DEMs to estimate the spatial distribution of unit stream power in two contrasted watersheds in Quebec: a small agricultural stream (Des Fèves River) and a large gravel‐bed river (Matane River). For slope, the centreline extracted from the raw LiDAR DEM was resampled at a coarser resolution using the minimum elevation value. The channel width extraction algorithm progressively increased the centerline from the raw DEM until thresholds of elevation differences and slopes were reached. Based on the comparison with over 4000 differential global positioning system (GPS) measurements of the water surface collected in a 50 km reach of the Matane River, the longitudinal profile and slope estimates extracted from the raw and resampled LiDAR DEMs were in very good agreement with the field measurements (correlation coefficients ranging from 0 · 83 to 0 · 87) and can thus be used to compute stream power. The extracted width also corresponded very well to the channel as seen from ortho‐photos, although the presence of bars in the Matane River increased the level of error in width estimates. The estimated maximum unit stream power spatial patterns corresponded well with field evidence of bank erosion, indicating that LiDAR DEMs can be used with confidence for initial hydro‐geomorphological assessments. Copyright © 2013 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it