Video‐monitoring of wood discharge: first inter‐basin comparison and recommendations to install video cameras
Bibliographic record
Abstract
Abstract Wood in rivers plays a major role both ecologically and morphologically. In recent decades, due to human activities in the river channels and along the riparian zone, wood obstruction and jamming has exacerbated flooding hazards and infrastructure damage. Therefore, it is necessary to quantify the wood flux and discharge in rivers to improve wood hazard management. Among the various methods for monitoring the wood flux in a river, the streamside videography technique is effective given its high temporal and spatial resolution. Previous work monitored the wood discharge (m3/s) using this technique in the Ain River (France) during three floods (MacVicar and Piégay, 2012), and the same method is implemented on the Isère River (France) to obtain the statistics of wood discharge for two floods. Comparison between the two sites supports the generalization of both the monitoring technique and the link between wood discharge and flood characteristics. We first show that the maximum wood discharge is observed at bankfull discharge, and we confirm the three stage model proposed by MacVicar and Piégay (2012). Additionally, transverse distributions of the number of wood pieces and corresponding wood length appear to be similar for different flood magnitudes on each site. As a technical contribution, the use of the same technique on two sites allows for recommendations on key decisions related to the location and implementation of the equipment. Both statistical and technical contributions can be used by decision makers to implement this monitoring technique, acquire the wood transport parameters, and evaluate the potential wood hazards at local scale or along a river. © 2020 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".