Behaviour and physiology of mountain whitefish (<i><scp>P</scp>rosopium williamsoni</i>) relative to short‐term changes in river flow
Bibliographic record
Abstract
Abstract Despite the growing recognition that river flow can have an effect on the growth, distribution and survival of fishes, little is known about the underlying mechanisms to explain this effect. Furthermore, there are few examples of integrated measures of behaviour and physiology to study the responses of fish to river hydrology. Here, axial swimming muscle electromyograms were logged as a sensitive indicator of activity from 19 mountain whitefish ( Prosopium williamsoni) across a large range of hourly discharge magnitudes (mean = 621 m3·s−1, range = 0–1770 m3·s−1) in a hydropeaking reach of the Columbia River, Canada. Hourly mean discharge had a significant positive effect on swimming muscle activity. However, a large amount of the variance was unexplained, possibly due to social interactions, feeding and/or flow‐refuging behaviours. Fluctuating flows were no more energetically costly than stable flows. Discharge magnitude had a significant positive effect on blood cortisol concentrations. Yet, cortisol concentrations were low overall (mean ± SD = 1.60 ± 0.09 ng·ml−1), suggesting that the small observed response could be the result of routine physiological processes rather than a stress response per se. Based on low blood lactate concentrations, mountain whitefish were not swimming exhaustively (i.e., anaerobic burst‐type swimming) at high flows.
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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".