Diel activity and foraging mode of juvenile Arctic charr in fluctuating water flow
Bibliographic record
Abstract
Abstract Streams fluctuate in water flow because of natural (e.g., rain) and human‐induced events (e.g., hydropeaking). Magnitude, frequency, and predictability of these events can have drastic consequences for fish populations. We studied how rapid modifications of water flow affect diel activity and foraging mode of juvenile Arctic charrSalvelinus alpinusin stream enclosures exposed to either stable (intermediate) or fluctuating (low vs. high) water flow. Under stable conditions, Arctic charr showed limited activity (9.4%). In fluctuating water flow, charr increased activity during low flow periods, especially during the first hours after the flow decreased, but ceased activity almost completely at high flow. Charr were mostly nocturnal, and more nocturnal at low than intermediate water flow. Fish were more mobile and swam faster during prey search and attacked prey at longer distances at low water flow. Activity and foraging mode differed between the first and second day after reduced water flow, suggesting that Arctic charr require time to adjust their foraging behaviour. This study demonstrates the importance of behavioural flexibility for population ecology in fluctuating environments such as regulated rivers.
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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".