Behaviour of Atlantic salmon smolts approaching a bypass under light and dark conditions: Importance of fish development
Bibliographic record
Abstract
The development of passage systems for migratory fish is crucial to mitigate the impact of river fragmentation. Concerning downstream migration of juvenile salmon (smolts), understanding their behaviour is a key to improving the efficiency of bypass systems. Among devices to improve efficiency, artificial lighting has proved effective in certain situations. Based on (1) recent observations of early migrating smolts where migration was delayed in the Poutès dam reservoir (Allier River, France) and (2) the fact that the implementation of bypass lighting devices was based on experiments involving later-season migrants, the present study assessed the effect of a lighting device on wild early-migrating smolts. One hundred wild smolts were tagged with acoustic transmitters and their behaviour near the bypass entrance under lit or dark conditions was assessed using 2D acoustic telemetry . A very abrupt change in behaviour around mid-April was observed, which directly affected their response to light. In the first phase of the downstream migration season (before mid-April), lighting significantly reduced the attractiveness of the bypass, while this surprisingly seemed to favour passage: smolts less frequently approached the bypass entry zone but passed through it more frequently. However, in the second phase (after mid-April), lighting attracted and kept the smolts close to the bypass entrance and significantly increased passage, corroborating previous experiments. The present study demonstrated an interaction between the development of migratory fish and their behaviour under lit or dark conditions. It also highlighted the importance of taking account of such behavioural change during the migration season when designing fish passage systems.
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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".