Empirical characterization factors to be used in LCA and assessing the effects of hydropower on fish richness
Bibliographic record
Abstract
Hydropower is often presented as a clean, reliable, and renewable energy source, but is also recognized for its potential impacts on aquatic ecosystem biodiversity. We used direct empirical data of change in fish species richness following impoundment to develop ecological indicators to be used in Life Cycle Assessment (LCA), and accounting for hydropower impacts on aquatic ecosystems. Data were collected on 89 sampling stations (63 stations located upstream, and 26 located downstream of a dam) distributed in 26 reservoirs from three biomes (boreal, temperate and tropical). Overall, the impact of hydropower on fish species richness was significant in the tropics, of smaller amplitude in temperate biome and minimal in boreal biome, stressing the need for regionalisation when developing indicators. The impact of hydropower was consistent across scales for a given biome (same directionality and statistical significance across sampling stations and reservoirs). However, the indicators were sensitive to the duration of the study (the period over which data have been collected after impoundment), which can underestimate the impacts. This result highlights the need to account for the duration of the transient dynamics to reach a steady state (rate of change in species richness = 0) before developing ecological indicators. By using the LCA approach, our suggested indicators contribute to fill a major gap in assisting decision-makers when evaluating the potential of alternative energy technologies, such as hydropower, to decarbonize the worldwide economy, while minimizing the impacts on aquatic ecosystems.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".