River metabolic fingerprints and regimes reveal ecosystem responses to enhanced wastewater treatment
Bibliographic record
Abstract
Although many studies have examined how improvements in wastewater treatment impact river nutrient concentrations and loads, there has been much less focus on measuring river metabolism to evaluate the wider aquatic ecosystem benefits of reducing nutrient inputs to rivers. The objectives of this study were to evaluate the effects of enhanced wastewater treatment (nitrification) on river metabolism in the Grand River, Canada's largest river draining into Lake Erie. Metabolic fingerprints and regimes (calculated from high-frequency dissolved oxygen [DO] measurements) were used to visualize whole-river ecosystem functional responses to these wastewater treatment upgrades. There was a 60% reduction in ecosystem respiration during summer, in response to reductions in effluent total ammonia inputs, causing a shift from net heterotrophy to net autotrophy, and contraction of river metabolic fingerprints. This resulted in major improvements in summer DO concentrations, with reductions in the percentage of days during summer that DO minima fell below water-quality guidelines for protection of aquatic early life stages, from 88% to ≤16%. The results also point to potential cascading impacts on coupled phosphorus and nitrogen cycles, which may generate further improvements in river water quality. During the summer, high rates of river metabolism and nutrient retention may result in measured water-column nutrient concentrations potentially underestimating nutrient pressures. This study also demonstrates the value of combining river metabolism with nutrient monitoring for a more holistic understanding of the role of nutrients in river ecosystem health and function.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".