Adfluvial migration and passage of Steelhead before and after dam removal at a major Great Lakes tributary
Bibliographic record
Abstract
Despite the importance of Great Lakes fisheries and the increasing popularity of dam removal as a method to restore river connectivity and increase fish passage, the adfluvial migration of Steelhead (Oncorhynchus mykiss) has been drastically understudied and only relatively few published studies have examined the impacts of dam removal on fish movement and timing. To help fill these knowledge gaps, spawning migrations of Great Lakes adfluvial Steelhead tagged in spring and fall were monitored for two years before and three years after removal of a dam that partially blocked upstream movement for 100 years. Removal of the dam not only reduced downstream delay and increased passage at the site of the dam removal itself, but increased travel speeds and increased passage at remaining upstream dams for both spring and fall run fish, underscoring the cumulative impact of successive dam passage on fish migration. Fall fish were most impacted by the dam removal and were able to pass not only the dam footprint, but, for the first time, were also able to pass both remaining upstream dams, allowing them to overwinter at locations closer to known spawning areas. For these fish, delay at the footprint was reduced from the order of 100+ days to < 1 hour and the number of days passage occurred compared to the number of days fish were present and blocked increased from 2% to 82%. The benefits of dam removal should ultimately equate to increases in fish production, as more critical habitat becomes reconnected and more fish are able to pass dams and arrive at spawning beds. The observation that fish are likely able to remain in better condition and retain more energy for continued migration and reproduction warrants further study.
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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".