Using GIS and a digital elevation model to assess the effectiveness of variable grade flow diversion terraces in reducing soil erosion in northwestern New Brunswick, Canada
Bibliographic record
Abstract
Abstract Flow diversion terraces (FDT) are commonly used beneficial management practice (BMP) for soil conservation on sloped terrain susceptible to water erosion. A simple GIS‐based soil erosion model was designed to assess the effectiveness of the FDT system under different climatic, topographic, and soil conditions at a sub‐basin level. The model was used to estimate the soil conservation support practice factor ( P ‐factor), which inherently considered two major outcomes with its implementation, namely (1) reduced slope length, and (2) sediment deposition in terraced channels. A benchmark site, the agriculture‐dominated watershed in northwestern New Brunswick (NB), was selected to test the performance of the model and estimated P ‐factors. The estimated P ‐factors ranged from 0·38–1·0 for soil conservation planning objectives and ranged from 0·001 to 0·45 in sediment yield calculations for water‐quality assessment. The model estimated that the average annual sediment yield was 773 kg ha −1 yr −1 compared with a measured value of 641 kg ha −1 yr −1 . The P ‐factors estimated in this study were comparable with predicted values obtained with the revised universal soil loss equation (RUSLE2). The P ‐factors from this study have the potential to be directly used as input in hydrological models, such as the soil and water assessment tool (SWAT), or in soil conservation planning where only conventional digital elevation models (DEMs) are available. Copyright © 2009 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".