Swimming behaviour and ascent paths of brook trout in a corrugated culvert
Bibliographic record
Abstract
Abstract Culverts may restrict fish movements under some hydraulic conditions such as shallow flow depths or high velocities. Although swimming capacity imposes limits to passage performance, behaviour also plays an important role in the ability of fish to overcome velocity barriers. Corrugated metal culverts are characterized by unsteady flow and existence of low‐velocity zones, which can improve passage success. Here, we describe swimming behaviour and ascent paths of 148 wild brook trout in a 1.5‐m section of a corrugated metal culvert located in Raquette Stream, Québec, Canada. Five passage trials were conducted in mid‐August, corresponding to specific mean cross‐sectional flow velocities ranging from 0.30 to 0.63 m/s. Fish were individually introduced to the culvert and their movements recorded with a camera located above the water. Lateral and longitudinal positions were recorded at a rate of 3 Hz in order to identify ascent paths. These positions were related to the distribution of flow depths and velocities in the culvert. Brook trout selected flow velocities from 0.2 to 0.5 m/s during their ascents, which corresponded to the available flow velocities in the culvert at the low‐flow conditions. This however resulted in the use of low‐velocity zones at higher flows, mainly located along the walls of the culvert. Some fish also used the corrugations for sheltering, although the behaviour was marginal and did not occur at the highest flow condition. This study improves knowledge on fish behaviour during culvert ascents, which is an important aspect for developing reliable and accurate estimates of fish passage ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".