Bibliographic record
Abstract
Addressing the impact of dams and other water control structures on fish communities and aquatic ecosystems is a major concern for fisheries managers in the Laurentian Great Lakes. Although nature-like and technical fishways (i.e., vertical slot, pool and weir, Denil) and, when suitable, barrier removals have been implemented across the basin, these fish passage applications are vastly outnumbered by barriers to fish movement. Lowermost barriers are the first structure that blocks fish passage within a tributary; and, in the Laurentian Great Lakes, they present a unique situation where restricting access to upstream habitat is a major component of a half a century long strategy to control invasive sea lamprey Petromyzon marinus. Solutions for passage at lowermost barriers must therefore consider alternative management actions surrounding increased connectivity and invasive species control. These actions are underlined by the primary management objective of enhancing production/diversity of native and recreationally desirable fishes. This review surveys the current state of fish passage technologies deployed in the Laurentian Great Lakes and other fish passage solutions under development, providing a reference for resource managers making decisions about barriers and fish passage that are critical for invasive species control and fishery restoration.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".