Bedform characteristics in a gravel-bed river
Bibliographic record
Abstract
Abstract Estimation of hydraulic and geometric parameters of a gravel-bed river such as dimensions of bedforms is very difficult task, although they play a fundamental role in river engineering projects. One of the methods to get essential information regarding the bedform characteristics is to find the relations between the flow parameters and bedform dimensions. We conducted this field study in the Babolroud River in northern Iran to investigate the application of double averaged method in unspecific gravel bedforms to evaluate friction factor. Using data collected from several river reaches with total length of 356 m of a gravel-bed river, the relationship between bedform geometry (height and the length of bedforms) and flow parameters including shear velocity, transport stage parameter with friction factor is investigated. Different methods for estimating bedforms dimensions are examined to assess the ability of predicting bedform parameters (length and height) in a gravel-bed river. Using bedform parameters, the contribution of particle and form friction is estimated. Results confirm the application of the double averaged method and existing bedform parameters for unspecific bedforms. There exists a similar trend between aspect ratio and friction factor in gravel bedforms.
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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 | 0.001 | 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)
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".