Implementation and validation of video monitoring for wood budgeting in a wandering piedmont river, the Ain River (France)
Bibliographic record
Abstract
ABSTRACT The transport of wood in rivers during floods is an important process that underlies differences in habitat and morphology between water courses and regions. Quantitative data are needed to properly address management objectives and balance wood budgets. In this study we use a streamside video camera to detect wood passage and measure quasi‐instantaneous rates of wood transport in the Ain River, France. The objectives are to verify the procedure, describe the relation between wood transport and discharge, and construct and validate a wood budget for the reach upstream of the camera. Verification of the procedure includes tests of detection frequency, wood velocity, and piece size. A log base two transformation is proposed to classify wood by piece length. It was found that a wood transport threshold occurs at approximately two thirds of the bankfull discharge. Wood transport follows a positive linear relation with discharge up to the bankfull discharge but is both more variable and less sensitive to discharge when the floodplain is inundated. Transport rates are approximately four times higher on the rising limb of the hydrograph than on the falling limb. Wood transport estimates from a three‐stage rating curve are two to 10 times higher than those from a wood budget using local and aerial surveys of upstream dynamics. Future work should address uncertainties related to wood diameter measurements, sampling length and frequency, and antecedent floods. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".