Predicting Lake Quality for the Next Generation: Impacts of Catchment Management and Climatic Factors in a Probabilistic Model Framework
Bibliographic record
Abstract
Lake ecosystems across the world are under combined pressures of eutrophication and climate change, which increase the risk of harmful cyanobacteria blooms, reduced ecological status, and degraded ecosystem services. In Europe, the third cycle of river basin management plans (2021–2027) according to the Water Framework Directive must take into account the potential impacts of climate change on water quality, including effects on relevant biological indicators. Here, we applied a Bayesian network as a meta-model for linking future climate and land-use scenarios for the time horizon 2050–2070, via process-based catchment and lake models, to cyanobacteria abundance and ecological status of a eutrophic lake. Building upon previous applications of the model, a new version was developed to include relevant climatic variables such as wind speed. Explorative scenarios showed that the combination of low wind and high temperature gave the most synergistic effects on cyanobacteria under high levels of eutrophication (Chl-a concentration). Considering the management target of good ecological status, however, the climate-related promotion of cyanobacteria blooms contributed most to degrading the ecological status at intermediate levels of eutrophication. Future developments of this model will aim to strengthen the link between climate variables and ecological responses, to make the model also useful for seasonal forecasting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".