Effectiveness monitoring of fish passage facilities: historical trends, geographic patterns and future directions
Bibliographic record
Abstract
Abstract Fishways and other passage facilities frequently prevent or delay the passage of fishes, highlighting the need for effectiveness monitoring. We reviewed the scientific literature from 1960 to 2008 reporting on effectiveness monitoring of fish passage facilities to assess what taxa and life‐stages have been studied, the questions that are asked during evaluation, and how these varied over time or by geographic region. We identified 96 peer‐reviewed articles of which 68% focused on passage by adult fishes. Salmoniformes was the most studied order (58% of studies). The focus of fishway evaluations did not change over the years, but varied significantly by geographic region. Studies from the tropics had a broader taxonomic scope than studies from temperate locations. Exogenous mechanisms of passage failure, such as environmental, structural and behavioural factors, were studied in 90% of studies from North America but only ∼50% of studies from Europe, South America and Australia. Endogenous (i.e. physiological) mechanisms affecting passage success were not often assessed anywhere, though they were a powerful means of evaluating mechanisms of failure. Few studies monitored migration after fish had left a facility. To improve effectiveness monitoring of passage facilities, we suggest that both endogenous and exogenous mechanisms need to be studied in an integrated fashion to understand passage failure and to inform design or operational changes that could improve passage efficiency. In addition post‐departure monitoring is required to more completely assess the fitness consequences of 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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".