The Effects of Run-of-River Hydroelectric Power Schemes on Fish and Invertebrate Community Composition in Temperate Streams and Rivers
Bibliographic record
Abstract
Run-of-river (ROR) hydroelectric power schemes are often presumed to be less environmentally-damaging than large-scale storage schemes. However, there are currently only a limited number of peer-reviewed studies on their physical and ecological impact. This presentation will summarise the findings from a policy secondment, funded by the UK’s Natural Environment Research Council and the Environment Agency of England, which investigated the impacts of ROR hydroelectric power schemes on fish and invertebrate communities in temperate streams and rivers, using Before-After, Control-Impact (BACI) study designs. The study made use of routine environmental surveillance data collected as part of long-term national and international monitoring programmes at systematically-selected ROR hydroelectric power schemes and systematically-selected paired control sites. Five metrics of invertebrate community composition and six area-normalised metrics of fish community composition were analysed using linear mixed effects models. The results are discussed with respect to impacts from other sources of power, and recommendations are made for best-practice study design for future freshwater community impact studies.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".