To swim or to jump? Passage behaviour of a potamodromous cyprinid over an experimental broad‐crested weir
Bibliographic record
Abstract
Abstract Physical stressors, such as man‐made obstacles, are considered one of the main causes that negatively affect freshwater fish. Even small weirs may impact fish populations, including potamodromous cyprinids, by partially or totally blocking upstream migratory movements. Some studies have addressed the effect of key hydraulic parameters on upstream movements past small weirs, but little is known on how these parameters interact to induce swimming or jumping behaviour in negotiating such obstacles. This study aims to evaluate the passage behaviour (swimming vs. jumping) of Iberian barbel (Luciobarbus bocagei), a potamodromous species, over an experimental small broad‐crested weir, considering the effect of different plunge pool depths (D), waterfall heights (H), and weir discharges (Q). Results revealed that passage behaviour was highly dependent on combinations of plunge pool depths and waterfall heights. Barbel navigated most configurations by swimming (overall outcomes: 81.5% swimming vs. 18.5% jumping), except the ones with the higher waterfall heights (25 cm) tested. Therefore, higher waterfalls proved to be preponderant in the switching of passage behaviour from swimming to jumping. Regarding the discharge over the 1‐m‐wide weir (overall outcomes: 85% swimming vs. 15% jumping), contrary to what was expected, there was no evidence that passage behaviour was discharge‐related, for the range of discharges tested (25–100 L/s). These results are useful to identify potential migration obstacles and should be taken into consideration in river restoration projects and when designing fishways for potamodromous cyprinids.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".