Habitat Choice by Atlantic Salmon Parr in Relation to Turbulence at a Reach Scale
Bibliographic record
Abstract
Abstract The variables commonly used to describe the physical habitat of Atlantic salmon Salmo salar parr are average velocity, water depth, and substrate. A variety of micro- and mesohabitat models have been developed using these variables to assess habitat quality. However, Atlantic salmon parr live in highly turbulent streams and rivers in which intense fluctuations of water velocity occur. Laboratory experiments have shown that turbulence affects the behavior and energetics of fish. Nevertheless, habitat use in relation to the strong temporal variability of velocity in natural environments has rarely been studied. In this study, Atlantic salmon parr habitat was examined in relation to turbulence in the Patapédia River, Quebec. Rather than taking the usual approach of surveying a large population at one point in time, we used an intensive radiotelemetry tracking survey that focused on the habitat use of a few individual fish over an extended period. We analyzed habitat use in relation to several dynamic hydraulic variables. Our results revealed that under naturally turbulent conditions, the parr displayed high individual variability in their habitat use. Such heterogeneous use of habitat suggests that individuals are not constrained to a single habitat type. Furthermore, no differences were observed in habitat use among the four daily periods (dawn, day, dusk, and night) for 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) | 0.002 | 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".