Bed morphology response to bedload transport in a small gravel-bed stream
Bibliographic record
Abstract
Forms such as riffles, pools and bars are the main large scale features structuring the bed in many gravelly streams. The occurrence and evolution of these forms are known to be directly linked to the bedload processes by a complex set of influences and retroactions. Estimating accurately bedload transport in gravel-bed streams is still a challenge for fluvial geomorphologists and engineers. Successive morphological surveys before and after flood events are often used to estimate bedload transport rates. This method provides an indirect knowledge on the interactions between morphology and bedload. The objective of this study is to document the morphologic response of a small stream to a sequence of bed load transport events and to compare the morphological method for estimating bedload transport with direct measurements obtained with sediment traps. Beard Creek presents a sequence of autogenic pools and riffles. Bankfull channel width is 5 m and bankfull discharge is 2 cumecs. Median b-axis of the bed surface grains is 40 mm. Three bedload sediment traps measuring real-time rates with load cells were installed in the river bed. A morphological survey followed each flood that occurred during two years and the sediments in the traps were also sampled and sieved. The morphological data was complemented with a survey of bed activity, which consists in estimating the displacement of tags distributed on a one square meter grid 20 meters upstream from the traps. Fifteen effective floods were surveyed and the flood peaks varied between 0.7 and 7 cumecs.
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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 | 0.000 | 0.000 |
| 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".