Hydropower Effluent as a Marine Pollutant; Impacts of River Regulation on Estuarine and Coastal Ecology
Bibliographic record
Abstract
ABSTRACT The confluence of rivers with the ocean creates biological hotspots where temperature, salinity, and nutrients mix to provide excellent conditions for rearing, growth, and refuge to a multitude of organisms. Worldwide, estuaries are highly productive and biodiverse. However, estuaries are also highly degraded by development and pollution due to human settlement and exploitation of waterways. As global energy systems transition to renewable energy sources, rivers are increasingly stressed by regulation to produce power. Effluent water flowing from rivers is channeled through the river and eventually reaches the ocean, meaning that power production can dramatically alter the dynamics of freshwater input at estuaries, including marked changes in the annual cycle of discharge. Despite much research focusing on understanding and mitigating the impacts of hydropower production on river ecosystems, less research has been conducted to understand how alterations to the flow regime affect marine ecosystems where the water is discharged. We address the status of knowledge at the interface of freshwater and marine ecosystems and highlight research priorities to better understand how sensitive estuarine and coastal habitats are affected by thermal, osmotic, and physical changes caused by hydropower production in rivers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".