The relative stability of salmon redds and unspawned streambeds
Bibliographic record
Abstract
Abstract Where female salmon build nests (“redds”), streambed material is mixed, fine sediment is winnowed, and bed material is moved into a tailspill mound resembling the shape of a dune. Completed redd surfaces are coarser and better sorted than unspawned beds, which is thought to increase redd stability because larger grains are heavier and harder to move, and sorting increases friction angles for mobility. However, spawning also loosens sediment and creates topography that accelerates flow, which can increase particle mobility. We address these factors controlling the relative stability of redds and unspawned beds in flume experiments where redds were constructed with a dynamic technique that mimics the nesting behavior of female salmon. Although redds exhibited relatively coarse surfaces, measured entrainment forces indicate particle loosening by spawning lowered grain resistance to motion by 12–37% on average compared to unspawned beds. In addition, for the same discharges, boundary shear stress was 13–41% higher on a redd due to flow convergence on the tailspill. Visual measurements of particle entrainment further indicated redd instability, as bed‐average shear stress was 22% lower at incipient motion and 29% lower at the discharge that mobilized all grain sizes on a redd. Overall, results demonstrate that redds are unstable compared to unspawned beds, which increases the risk of scour for buried eggs but may facilitate fine sediment flushing and improve the quality of spawning gravels for future generations of spawners. Therefore, managing salmon returns to increase streambed disturbance may be an effective tool for reducing sedimentation impacts on salmon reproduction.
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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".