Stream Gradient‐Related Movement and Growth of Atlantic Salmon Parr during Winter
Bibliographic record
Abstract
Abstract There has been considerable focus on winter studies of Atlantic salmonSalmo salarparr during the last two decades. However, a lack of knowledge exists about the linkage between the physical conditions, including ice, and parr behavior in flow environments during the cold season. In this study, the movement and growth of Atlantic salmon parr were studied during winter in two stream sections with different gradients (0.3% and 1.8%) in a small natural river. Passive integrated transponder (PIT) technology was implemented by using both fixed antennae and mobile tracking devices. In the low‐gradient section, the formation of surface ice dominated and created stable conditions, whereas in the high‐gradient section, the formation of anchor ice and anchor ice dams occurred periodically and produced a dynamic environment. The results indicated a relationship between parr movement and river gradient. Movement by parr was negatively correlated with time from autumn to late winter but increased as spring approached. The level of movement was considered low (median = 0.9 m), but larger movements (up to 125.6 m) were recorded, indicating individual variation. Furthermore, parr inhabiting the low‐gradient section with the static ice formation exhibited larger movement than parr in the high‐gradient section with dynamic ice formation. Parr showed high site fidelity to both high‐ and low‐gradient sections but were less attracted to pool habitats. Finally, from late autumn to spring, parr demonstrated a specific growth rate close to 0.0%, indicating suitable conditions in both sections. As this study implies, the complexity of physical conditions during winter may lead to variability in individual fish response (i.e., movements). However, the results also imply that winter is not a limiting factor in parr performance if fish have access to suitable cover, such as areas with low substrate embeddedness.
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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 | 0.000 |
| 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".