Estimation of bed material transport in gravel‐bed streams using the virtual velocity approach: Insights from the North‐Western Himalayas, India
Bibliographic record
Abstract
Abstract This study focuses on evaluating the sediment mobility and transport patterns in two Himalayan rivers (Aglar and Paligad Rivers) during monsoon and non‐monsoon flows. The virtual velocity approach involving the measurements of the bed proportional mobility (Y), active layer depth (ds), displacement length and virtual velocity of mobilized grains was employed. Both local (0.5 m subsections) and wetted cross‐sectional average parameters were used. While using local parameters the total annual bed material transport was estimated to be 67 100 (±20 400 t) and 18 400 t (±6000 t) in the Aglar and Paligad Rivers, respectively. Of this, nearly 60% of transport occurred during the monsoon and the overall contribution of partial transport (PT) remained low (<6%). However, based on cross‐section average parameters, total transport was estimated to be 42 300 (±15 800 t) and 12 200 t (±4700 t), in Aglar and Paligad, respectively, with nearly 79% and 68% occurring during the monsoon. Moreover, the contribution of PT increased to nearly 18% and 29% for the Aglar and Paligad Rivers, respectively. Additionally, the dependence of PT on Y and full transport on ds results in an abrupt shift in transport rates at the transition from partial to full transport, causing discontinuity in transport curves. Therefore, a unified function was proposed to represent the extent of transport for both partial and full transport, yielding continuous transport curves. These findings are particularly relevant for efficient river management as the region houses several hydropower plants and is highly vulnerable to climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".