Do artificial velocity refuges mitigate the physiological and behavioural consequences of hydropeaking on a freshwater Iberian cyprinid?
Bibliographic record
Abstract
Abstract The rapid flow fluctuations experienced downstream of hydropeaking facilities can alter the river hydromorphology. Given the dependence of riverine fish on physical habitat, those alterations have the potential to change the physiology and behaviour of fish. We assessed whether artificial velocity refuges mitigated the physiological and behavioural consequences of hydropeaking for the Iberian barbel (Luciobarbus bocagei). Hydropeaking trials were conducted in an indoor flume equipped with deflectors that created low flow velocity areas to serve as refuges. The FLOW‐3D was used to obtain detailed characterizations of the different velocity fields, which facilitated the interpretation of fish responses. Changes in flow magnitude and duration triggered stress responses, demonstrated by the increased blood glucose levels in the single up‐ramping event for 60 L s−1and in the step up‐ramping event. Fish tended to seek out velocity refuges to avoid higher flow velocities and harsh hydraulic conditions at peak flows, and during the longer events. The movement behaviour frequency increased when fish were subjected to the highest peak flow (60 L s−1), particularly the individual sprints and the drifts. For the base flow (7 L s−1) and the lowest peak flow (20 L s−1) conditions, fish swam freely in the flume, whereas in the harshest hydraulic conditions they showed more difficulty in finding velocity refuges. This research presents a novel approach by combining physiology and behavioural observations with hydraulic modelling to assess the extent to which artificial flow refuges mitigate the consequences of hydropeaking. Our work serves as a model approach for future mitigation studies for fish in hydropeaking 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 | 0.001 |
| 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".