Assessment of erosion and settling properties of fine sediments stored in cobble bed rivers: the Arc and Isère alpine rivers before and after reservoir flushing
Bibliographic record
Abstract
Abstract Cohesive sediment dynamics in mountainous rivers is poorly understood even though these rivers are the main providers of fine particles to the oceans. Complex interactions exist between the coarse matrix of cobble bed rivers and fine sediments. Given that fine sediment load in such environments can be very high due to intense natural rainfall or snowmelt events and to man‐induced reservoir or dam flushing, a better understanding of the deposition and sedimentation processes is needed in order to reduce ecohydrological downstream impacts. We tested a field‐based approach on the Arc and Isère alpine rivers combining measurements of erosion and settling properties of river bed deposits before and after a dam flushing, with the U‐GEMS (Gust Erosion Microcosm System) and SCAF (System Characterizing Aggregates and Flocs), respectively. These measurements highlight that critical shears, rates of erosion, settling velocities and propensity of particles to flocculate are highly variable in time and space. This is reflective of the heterogeneity of the hydrodynamic conditions during particle settling, local bed roughness, and nature and size of particles. Generally the deposits were found to be stable relative to what is measured in lowland rivers. It was, however, not possible to make a conclusive assessment of the extent to which the dynamics of deposits after reservoir flushing were different from those settled after natural events. The absence of any relationships between erosion and deposition variables, making it impossible to predict one from another, underlined the need to measure all of them to have a full assessment of the fine sediment dynamics and to obtain representative input variables for numerical models. While the SCAF was found to be effective, an alternative to the U‐GEMS device will have to be found for the erodibility assessment in cobble bed rivers, in order to make more rapid measurements at higher shears. Copyright © 2017 John Wiley & Sons, Ltd.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".