Changes in chlorophyll-a in English rivers over the last 49 years
Bibliographic record
Abstract
• Used 49 years of data from 161 rivers to understand trends in eutrophication. • Bayesian modelling allowed greater precision and sensitivity in analysis • No clear change in algal bloom events across England in over the last 49 years. Ongoing anthropogenically-driven environmental change in rivers (e.g. increasing air temperature, changing river flow extremes, increases in some key nutrients and decreasing concentrations of other key nutrients) is expected to impact ecological status and the magnitude and frequency of river algal blooms. In this study we considered 49 years of data from up to 161 river sites across England using water-column chlorophyll-a as a measure of suspended algal biomass and used a Bayesian hierarchical model to explore the potential drivers of changing river chlorophyll-a concentrations. Over a period of five decades the changes in chlorophyll-a concentrations in rivers across England showed a mixed pattern in relationships with key environmental variables and are almost evenly divided between significant increases and decreases in those chlorophyll-a concentrations. Most river sites showed no significant change in the probability of algal bloom events (chlorophyll-a > 15 μg/l; > 30 μg/l or 45 μg/l) over the last 49 years. These results indicate that there has been no clear directional response in algal bloom events across England’s rivers to the changing pressures, including climate change and large-scale reductions in P concentrations achieved over the last 49 years from improved wastewater treatment. By identifying these differing patterns in chlorophyll-a trends and responses across England, this large-scale spatio-temporal analysis provides a basis for exploring the multiple pressures driving chlorophyll-a responses at local to regional scales.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".