Hydrological Evaluation of Flow Diversion Terraces Using Downhill-Slope Calculation Method for High Resolution and Accuracy DEMs
Bibliographic record
Abstract
Slope is an important topographical parameter in hydrological models. Dramatic changes in slope are produced due to micro variations of topography in flow diversion terrace (FDT) systems. Assessing the effectiveness of FDT on soil conservation yields different results using different slope calculation methods with different resolution and accuracy digital elevation models (DEMs). In this study, the ArcGIS built-in average-neighborhood-slope (ANS) method and a downhill-slope (DHS) method were used to calculate slopes for a small agricultural watershed in New Brunswick, Canada. Six DEMs were used, i.e., 1, 5 and 10 m resolution DEMs generated with conventional photogrammetric techniques, and 1, 5, and 10 m resolution DEMs derived from light detection and ranging (LiDAR) data. Calculated slopes were summarized for the whole watershed, a patch of FDT systems, and grassed flow channels in front of embankments in the patch. Results indicated that the DHS method produced smaller slopes than the ANS method with LiDAR-1m DEM along grasses flow channels. Furthermore, the mean L-factor derived from the DHS method was smaller than that derived from the ANS in terraces and grassed flow channels for the LiDAR-1m DEM. Finally, soil loss estimated from the DHS method using RUSLE was more consistent with measurement for the watershed than that from the ANS method when the LiDAR-1m DEM was used. The DHS method can provide accurate slopes along grassed flow channels and can be used to assess FDT systems using USLE-based models with high resolution and accuracy DEMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".