Bibliographic record
Abstract
Anthropogenic modifications to aquatic ecosystems have altered connecting pathways within, and in some cases, between watersheds. Human structures, such as hydroelectric facilities, often impede fish migrations and may inflict heavy mortality on fish that become impinged or entrained. Conversely, an increase in connectivity between two waterways (e.g., through the construction of shipping canals, increased boat traffic) often results in an elevated risk of invasive species introductions. Non-physical barriers, which obstruct fish from an undesirable location without influencing the waterway, are one management approach to protecting valuable fish stocks and deterring biological invasions. Because many methods of behavioral deterrence have been employed against fish, there is a need to summarize and compare existing and developing technologies. This review details the use and application of electrical, visual, acoustic, chemical, and hydrological deterrence techniques that may be used to prevent fish movements. Site requirements are discussed, and a critical assessment of benefits and limitations to each technique are given. Because no single method of fish deterrence is “one size fits all”, this review to non-physical fish barrier technology will benefit managers and researchers attempting to develop a best-fit strategy on a case-by-case basis.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".