Challenges in downstream dam passage and the effect of dam removal on <scp>A</scp>tlantic <scp>s</scp>almon (<scp><i>Salmo salar</i></scp>) smolt migrations
Bibliographic record
Abstract
Migration is critical for life-cycle completion in diadromous fish species. River connectivity is vital in facilitating these large-scale movement events, but the extent of present-day river fragmentation can interfere with these migrations. Fish passage solutions (FPSs) are commonly implemented with the aim of improving river connectivity. In our study, we investigated the performance of two types of FPSs, spill regimes and complete dam removal, on Atlantic salmon (Salmo salar) smolt migrations. We used acoustic telemetry to monitor migration behavior and passage success of 120 wild smolts released in three different groups/sites: one group with two dams to pass to reach the river mouth, a second group with one dam to pass, and a control group without any barriers to pass (upstream of a recently removed hydroelectric dam). Smolt passage probabilities were similar for the two studied dams (87% and 86%) but showed variation in path choice, delay times, and loss rates. Passage success was influenced by several factors, such as body size, diel period, and water temperature, but not flow. Cumulative passage success to the river mouth was 61%, with most individuals being lost within lentic river stretches, either in the forebays of hydroelectric power stations or in naturally wide river stretches. Within the recently rehabilitated river sections (post dam removal), passage speeds were significantly faster than all other sections of the river (post-rehabilitation x¯ = 56.1 km/day) with significantly faster speeds compared to pre-rehabilitation (pre-x¯ = 28.0 km/day). Our findings provide valuable information on the benefits of dam removal and highlight the need for further rehabilitation measures in upriver reaches where barriers still affect downstream passage.
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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".