Atlantic Salmon Upstream Migration Delay in a Large Hydropower Reservoir
Bibliographic record
Abstract
Abstract Spawning success of Atlantic Salmon Salmo salar is challenged when migratory routes to natal streams are obstructed by hydropower generation stations and reservoirs that lack directional cues, potentially causing migratory delay. This study used 74 acoustic-tagged adult Atlantic Salmon during their spawning migrations to quantify migratory success, rates, and delay through the Mactaquac Reservoir in the Saint John River, New Brunswick, during three migration seasons in 2014–2016. Tag loss or mortality was considerable, reducing the effective sample size to 34 successfully tracked adults. Of these, 41% experienced fallback over the dam, 12% were unsuccessful in exiting the reservoir, and 47% were successful in exiting the reservoir on the way to the spawning grounds. Migration rates were significantly slower in the reservoir (median ± SE = 9.3 ± 1.9 km/d) than upriver (39.0 ± 4.1 km/d), and the tagged Atlantic Salmon spent 31–53% of their time reversing direction and thus travelled longer distances (73 ± 58 km) than the minimum 37-km midline route through the reservoir. Traveling the distance of the reservoir at the upriver migration rate could have shortened their journey by a median of 3.8 d. Sensor tags indicated that individual Atlantic Salmon experienced temperatures of 10–20°C (median ± SE = 16.0 ± 0.03°C) and migrated at depths of 5–35 m (23.4 ± 0.1 m) within the reservoir. Given that some of the energy needed for reproductive development and activities was likely appropriated by migratory delay and that a moderate proportion (47%) of adults emigrated from the reservoir, volitional passage may not be a successful management strategy in the studied reservoir. However, a substantially reduced sample size negates conclusive remarks about which management strategy would maximize survival and spawning success, and further study is needed.
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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".