Jordan and Fanjoy, page 1 Sediment yields and sediment budgets of community water supply watersheds in southeastern British Columbia.
Bibliographic record
Abstract
Abstract: Over the past 10 years, the B.C. Forest Service has measured sediment concentration and turbidity on a number of creeks in the Kootenay region of British Columbia, which are used for community or domestic water supply. This paper summarizes the results of measurements on 11 forested watersheds. Some of them have streamflow stations, so that suspended sediment data collected for water quality purposes can be converted to sediment yield. Reasonable estimates of discharge and yield can be made for the remaining creeks. Both undeveloped and developed watersheds are included. For most watersheds, annual background suspended sediment yields are comparable to or slightly higher than the range of published Water Survey of Canada results for small forested watersheds (about 3 to 10 t/km2/y). These yields are lower than for most watersheds in British Columbia. Streams with low sediment yield have been chosen by communities as water sources because they provide good quality water. In some watersheds with forestry development, sediment yield is significantly greater than background levels, due mainly to erosion from logging roads. However, in most cases, the amount of sediment is still within generally accepted water quality guidelines. In rare cases, landslides caused by forest roads have resulted in very large increases in sediment yield.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".