The environmental impacts of run-of-river hydroelectric projects in British Columbia
Bibliographic record
Abstract
Run-of-river hydroelectric schemes have grown rapidly in British Columbia since BC’s 2002 Energy Plan was released. These projects are often claimed to be one of the most environmentally friendly methods of electricity generation, in particular as a tool to combat climate change. However, the body of literature on the subject highlights that these facilities have the potential to have wide-ranging impacts on the environment. Changes to abiotic factors in the aquatic system brought about by these projects include increased temperatures, reductions of in-stream flows, increased fine sediment concentrations and rapid changes to discharge. These abiotic alterations lead to biotic impacts: namely, reducing the quantity and quality of available fish habitat (in particular to Salmonids) as well as reducing the amount of aquatic invertebrates, a primary food source for fish. Moreover, to have these projects operational, they require dozens of kilometers of linear infrastructure, most notably rehabilitated resource roads and newly constructed transmission line networks to connect to BC Hydro’s grid. The upshot of my research indicates that run-of-river projects are not as environmentally benign as some would have the public believe, and it is uncertain whether their climate change mitigations trump their immediate aquatic and terrestrial impacts.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".