Does turbulence affect the habitat choice of Atlantic salmon parr?
Bibliographic record
Abstract
Habitat preferences of Atlantic salmon parr are commonly described using mean flow velocity, water depth, and substrate as habitat variables, and a variety of habitat models have been developed using these variables to predict habitat quality. However, Atlantic salmon parr live in highly turbulent streams and rivers, in which intense fluctuations of flow velocity occur. Habitat preferences that consider the high variability of flow velocity have not been studied, and this although it has been shown in laboratory experiments that turbulence may affect the behavior and energetics of fish. Consequently, we studied the use of turbulent flow by Atlantic salmon parr in Patapédia River, Québec, Canada using radio-telemetry. We analyzed summer habitat preferences of individual parr in relation to several dynamic hydraulic variables such as standard deviation of flow velocity, turbulent kinetic energy, Froude number, and shear stress, and compared them with the habitat availability within the river reach. Our results revealed that in a natural flow environment, parr display a high individual variability in habitat preferences in relation to flow turbulence. Such heterogeneous habitat preferences suggest that individuals are not constrained to single habitat types and exhibit flexible habitat use. Furthermore, no differences were observed in habitat preferences between the four daily periods (dawn, day, dusk, and night) within individual parr.
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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 | 0.000 | 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".