A 150-year river water quality record shows reductions in phosphorus loads but not in algal growth potential
Bibliographic record
Abstract
Eutrophication and proliferation of nuisance and harmful algal blooms are a major cause of water-quality impairment globally. Here, we analyze the world’s longest continuous (150-year) river water-quality dataset for the River Thames, U.K. (including biological oxygen demand, chloride, phosphorus and silica), to explore the impacts of urbanization, wastewater discharges and agricultural intensification. Over the last 40 years, improvements in wastewater treatment and agricultural management have reduced phosphorus loads by ~80%. However, this has been insufficient to curtail river algal blooms because nutrient concentrations remain above limiting levels. Over the last 50–60 years, rising water temperatures have increased the number of days with water temperatures favourable for diatom blooms in March-April and for cyanobacterial growth in July-August. These results highlight the challenges of eutrophication management in a warming climate and a strategic need to redouble efforts in further reducing nutrient emissions to control nuisance and increasingly harmful algal blooms. The world’s longest continuous river water quality record for the River Thames, UK, reveals that phosphorus loads have decreased by 80% over the past 40 years, but increasing water temperatures are leading to higher river algal growth potential.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".