Suspended sediment dynamics associated with snowmelt runoff in a small mountain stream of Lake Tahoe (Nevada)
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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.
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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) | 0.001 | 0.000 |
Machine scores (provisional)
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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 it