Controls on sediment production from an unpaved resource road in a Pacific maritime watershed
Bibliographic record
Abstract
Abstract Unpaved resource roads have the potential to produce large amounts of sediment and can negatively impact water quality and aquatic ecology. In order to better understand the dominant controls on sediment generation from unpaved resource roads, we did 23 large‐scale rainfall simulation experiments on a road section in the Honna Watershed, Haida Gwaii, British Columbia, Canada. The experiments were performed with different rainfall intensities (4–52 mm/h), with and without traffic. Precipitation intensity was the dominant control on the amount of sediment generated from the road surface; the total mass of sediment increased linearly with precipitation intensity. The number of passages of loaded logging trucks during an experiment was the second most dominant control on the total amount of sediment generated from the road surface. Elevated sediment concentrations in road surface runoff persisted for 30 min following the passage of loaded logging trucks during low intensity (<8 mm/h) rainfall events and for much shorter periods at higher rainfall intensities. The mass of sediment generated by the passage of a loaded truck increased with precipitation intensity. Passages of empty logging truck did not result in sediment pulses, except during very high rainfall intensities. Seven small‐scale rainfall simulation experiments on other parts of the road, however, highlight the large spatial variability in sediment production from the road surface, suggesting additional experiments are required to better describe and predict sediment production from different road sections.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".