Modelling changes in suspended sediment from forest road surfaces in a coastal watershed of British Columbia
Bibliographic record
Abstract
Erosion from logging road surfaces, cut slopes, banks, and ditches represents a chronic source of sediment input to streams that can degrade aquatic habitats. Road surface erosion is of particular concern because the magnitude of sediment generation when traffic levels are high can be large. Current models for predicting sediment production from roads require information on area-specific sediment delivery, which is not often available. Here, we developed a model to quantify suspended sediment concentrations (SSC) generated by forest roads surfaces under different conditions of use and density. This model is designed for a typical medium-size coastal watershed of British Columbia or the American Pacific Northwest, and was applied to the Chilliwack River watershed as a case study. The results illustrate that intensive use of forest roads combined with high road density can increase the number of extreme sedimentation events over a predetermined threshold. A comparison of the effects of road density and the level of road use suggests that the level of road use is more important than the road density for the generation of fine sediment from road surfaces. However, the model omits the impact of roads on mass movements in a watershed, which represent a major source of sediment in steep watersheds, so the effect of road density is likely more substantial than the model predicts. The model is an attempt to overcome field data limitations by using an empirical relation between SSC and traffic variables, and presents a starting point for more intensive field studies that could be used to validate it. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".