Finding a way upstream: environmental effects on immature American eel movement below a hydropower dam
Bibliographic record
Abstract
The abundance of the American eel is declining, in part due to the fragmentation of riverine habitat by human-made barriers like hydroelectric dams. Despite the importance of successful passage at barriers during upstream migration, little is known about the movement behavior of immature eels at hydroelectric dams and the potential effects of artificial environmental conditions. Here, we investigate the movement behavior of migrating yellow eel below the Carillon dam, the first barrier during their upstream migration in the Ottawa River (Canada). Using acoustic telemetry, we quantify individual variability in movement behavior, timing of arrival at the dam, retention near the structure, return rates downstream, and whether eels find the only passage route to upstream habitats under fluctuating environmental conditions. Our results highlight intraspecific variability of exploratory behavior downstream of the Carillon dam and effects of atmospheric pressure, water flow, and temperature. On average, eels spent 17.7 days near the structure, but none successfully passed the dam, despite some individuals (16%) attempting passage multiple times. Thus, the Carillon dam likely represents an important barrier to immature eels during upstream migration that potentially confounds environmental migratory cues, induces migratory delay, and ultimately reduces access to headwater habitat.
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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".