Influence of baffles on upstream passage of brook trout and brown trout in an experimental box culvert
Bibliographic record
Abstract
There is much to learn about improving baffle designs to increase successful fish passage through culverts. A fish’s motivation to attempt entry into the culvert is essential. Upon entry, successful passage will largely depend on the physiological ability of the fish to navigate the entire culvert length. In this study, the motivation of brook trout (Salvelinus fontinalis (Mitchill, 1814)) and brown trout (Salmo trutta Linnaeus, 1758) to attempt ascent of an experimental flume, which mimics a roadway culvert left bare (smooth) or fitted with either spoiler or weir baffles, is assessed. Performance, measured as maximum distance of ascent within the flume, is also quantified. The bare flume was the most motivating for brook trout, and the weirs were most motivating for brown trout. As a rule, brown trout showed less motivation to stage attempts than brook trout, except within the weir baffle treatments. Performance was greatest in the weirs for smaller trout and in the spoiler baffles for larger trout. Our findings suggest that baffle form influences passage rates at road crossings in ways previously unknown and further stresses the importance of considering fish motivation and performance together when assessing the efficacy of baffle forms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".