Ecological consequences of flow regulation by Run-of-River hydropower on salmonids
Bibliographic record
Abstract
Streams are dynamic, disturbance-driven ecosystems, where flow plays a dominant role structuring biological communities.Anthropogenic activities on streams change natural patterns of flow and disturbance, which in turn alters the conditions to which resident fishes are adapted, and their survival and fitness.Run-of-river (RoR) hydropower projects are an example of an anthropogenic activity that may alter stream ecosystems by temporarily diverting a proportion of stream flow to produce electricity.RoR hydropower projects have increased considerably in number and importance in the last three decades in both British Columbia and worldwide.Although there is a perception that RoR hydropower has minimal effects on stream ecosystems due to the small physical footprint of projects, we know surprisingly little about the impacts of RoR hydropower on fish populations.In this thesis, I use a combination of published research, empirical data, and models to evaluate a range of hypotheses regarding how RoR hydropower may affect fish populations, concentrating on salmonid species whose freshwater habitats often overlap with RoR projects.In Chapter 2, I synthesize the impact pathways by which RoR hydropower may influence salmonid populations, inferred from studies of reservoir-storage hydropower and salmonid ecology.In
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".