Suspended sediment dynamics associated with snowmelt runoff in a small mountain stream of Lake Tahoe (Nevada)
Bibliographic record
Abstract
Abstract The Lake Tahoe basin is experiencing an environmental decline that is partly due to sediment intakes from its tributaries. Many studies have estimated suspended sediment loads in these streams with a discrete sampling programme by collecting water samples and using a rating technique. However, the relationship between stream discharge and suspended sediment concentration (SSC) in these tributaries is known to differ during the rising and falling limbs of the snowmelt‐dominated hydrograph. Because of this hysteresis effect, sediment rating curves are poor predictors of suspended sediment dynamics in the stream. In this study, suspended sediment transport was investigated using a turbidity meter to provide a continuous record of sediment concentration during the snowmelt period. Hysteresis in suspended sediment transport was also investigated and is quantified with an H index, which is the ratio of the areas under the curve at different stages of the hydrograph. The temporal lag between the peak of SSC and the peak of stream discharge was quantified using cross‐correlation analysis. For almost all events, SSCs were higher during the rising limb of the hydrograph for a given discharge, with SSC peaks occurring before discharge peaks, resulting in clockwise hysteresis (H > 1). The H indices increased (looser hysteresis loop) as the availability of sediments increased and as the lag between peaks in SSC and discharge was larger. A restriction of the proposed H index was that it could only be computed when stream discharge increased by more than 30% during a melt event. Copyright © 2005 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".