Geomorphic controls, riffle substrate quality, and spawning site selection in two semi‐alluvial salmon rivers in the Gaspé Peninsula, Canada
Bibliographic record
Abstract
Abstract The relationships between valley and channel morphology, spawning substrate quality (content of fine sediment < 2 mm) and the selection of spawning sites by Atlantic salmon (Salmo salar) were investigated along 45 km of two semi‐alluvial, valley‐confined rivers in the Gaspé Peninsula, Canada. Linear and logistic regressions confirm that Atlantic salmon prefer spawning at riffles providing good rather than mediocre or poor spawning substrate, as defined by the percentage sand and the Sand Index of Peterson and Metcalfe. However, exceptionally large concentrations of redds were observed on the few riffles located at island heads, with sub‐optimal substrate quality. This observation suggests that, in addition to content of fine material in the substrate, the morphology of spawning reaches may be a significant factor controlling the intensity of inter‐gravel flow through redds and the consequent selection of spawning sites. In the study systems, the quality of spawning substrate was controlled by ‘large‐scale’ geomorphic attributes at the scale of valley segments (1–5 km here): segments located within a wide valley were actively meandering, had higher sinuosity and bank erosion rates, generally lower shear stresses and presented somewhat higher sand content than segments confined by a narrow valley. Although sand contents were significantly higher, laterally unstable segments in wide valleys still harboured good to excellent spawning substrate overall. The study data do not allow the roles of variations in levels of riffle‐zone shear stress to be distinguished from those of cut bank fines input, to explain the observed inter‐segment association between valley width and riffle fines content. Copyright © 2004 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".