Generalizing Trends in Upstream American Eel Movements at Four East Coast Hydropower Projects
Bibliographic record
Abstract
Abstract Dams impede the upstream migration of juvenile American Eel Anguilla rostrata, limiting their access to freshwater habitat and potentially contributing to population declines across their range. The implementation of fishways at large hydropower dams help restore access to upstream habitat and represents a long-term dataset of American Eel captures. We analyzed the relationships between eel captures and select environmental variables (river discharge, water temperature, and lunar illumination) at four hydropower projects on east coast rivers with a comparable decade of data and sampling techniques: Roanoke Rapids Dam on the Roanoke River in North Carolina, Conowingo Dam on the Susquehanna River in Maryland, Holyoke Dam on the Connecticut River in Massachusetts, and the Moses-Saunders Dam on St. Lawrence River in New York and Canada. The number of eels captured varied among projects, from year to year, and seasonally. American Eel are opportunistic in their upstream movements, with peak movement events associated with high flows, increased water temperature, and low lunar illumination. Our results suggest that systems altered by hydropower dams offer unique challenges to American Eel migrants and that a multitude of factors play a role in the timing of upstream movements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".