An Attempt to Take Into Account Natural Variability in 1D Bedload Prediction
Bibliographic record
Abstract
Abstract Bedload transport can fluctuate considerably over relatively short periods of time and for a given quasi‐constant flow rate. What are the implications of replacing the fluctuating signal with a smoothed signal when calculating bedload transport using averaged values, as is common practice? This question was investigated with the BedloadR code, which allows 1D bedload calculation as well as Monte Carlo simulations using a new data set collected in the Severaisse River (French Ecrins massif). Four bedload equations (Camenen & Larson, 2005, https://doi.org/10.1016/j.ecss.2004.10.019 ; Meyer‐Peter & Mueller, 1948; Parker, 1990, https://doi.org/10.1080/00221689009499058 ; Recking, 2013a, https://doi.org/10.1061/(asce)hy.1943‐7900.0000653 ) were selected for their performance relative to the measured bedload (except for and Meyer‐Peter and Mueller) and because each equation has a different mathematical form and degree of nonlinearity. They were used in a Monte Carlo approach, with input probability distributions fitted to the measured river width, slope, bed grain‐size distribution, and to the associated (computed) Shields stress. The results show that accounting for natural variability in the calculation reproduces bedload fluctuations well. But overall, when calculating the bedload volume transported by a flow event, accounting for variability systematically leads to higher estimated volumes (of the order of 20%) than those obtained with a deterministic approach using average input parameters. This is a direct consequence of the nonlinearity of the equations.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".