Discharge and hydraulic interactions in contrasting channel morphologies and their influence on site utilization by spawning Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
Six study sites were selected to represent the range of channel morphologies, extending from dynamic pool–riffle to transitional step–pool/plane bed reach types, used by spawning Atlantic salmon (Salmo salar) in a small upland Scottish stream. The hydraulic functioning of reaches over the range of discharges used by spawning fish was characterized, and the influence of hydraulic heterogeneity and rates of change in discharge on the frequency of spawning was assessed. Relationships between discharge and depths and velocities differed significantly between sites; thus, hydraulic responses to changes in discharge were different. The range of discharges used for spawning differed between sites, although optimum discharges were similar. Integration of hydraulic information with microhabitat suitability predicted that spawning conditions should occur at discharges higher than those utilized by fish. There was no evidence that hydraulically heterogeneous sites were used more frequently than homogeneous ones. Rather, data suggest that the frequency of utilization of sites was governed principally by the availability of suitable sediment. Flow stability was important for spawning, with periods of rapidly varying discharge avoided. It is suggested that the rate of change in discharge should be considered more explicitly when assessing environmental flow needs.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".