Fishway passage bottleneck identification and prioritization: a case study of Pacific lamprey at Bonneville Dam
Bibliographic record
Abstract
Fishways designed for salmonids often restrict passage by non-salmonids, and effective tools are needed both to identify passage problems for nontarget species and to inform remediation planning. In this case study, we used migration histories from 2170 radio-tagged adult Pacific lamprey (Entosphenus tridentatus) to identify locations of poor passage (bottlenecks) at a large, multifishway dam. Over 10 years, 49% of tagged lamprey that entered fishways failed to pass the dam. Models accounting for repeated attempts by individual lamprey indicated successful passage strongly depended on attempted passage route. Success also varied with time of fishway entry, water temperature, and lamprey body size. Most failed passage attempts terminated in lower fishway segments, but extensive seasonal shifts in bottleneck locations were detected. Ranking metrics helped prioritize bottlenecks and identified sites where structural or operational modifications should improve lamprey passage. Our integration of spatially intensive monitoring with novel analytical techniques was critical to understanding the complex relationships among fishway features, environmental variation, and lamprey behavior. The prioritization framework can be applied to a wide range of fish passage assessments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".