Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach
Bibliographic record
Abstract
Eutrophication of lakes and the risk of harmful cyanobacterial blooms due is a major challenge for management of aquatic ecosystems, and climate change is expected to reinforce these problems. Modelling of aquatic ecosystems has been widely used to predict effects of altered land use and climate change on water quality, assessed by chemistry and phytoplankton biomass. However, the European Water Framework Directive requires more advanced biological indicators for the assessment of ecological status of water bodies, such as the amount of cyanobacteria. We applied a Bayesian network (BN) modelling approach to link future scenarios of climate change and land-use management to ecological status, incorporating cyanobacteria biomass as one of the indicators. The case study is Lake Vansjø in Norway, which has a history of eutrophication and cyanobacterial blooms. The objective was (i) to assess the combined effect of changes in land use and climate on the ecological status of a lake and (ii) to assess the suitability of the BN modelling approach for this purpose. The BN was able to model effects of climate change and management on ecological status of a lake, by combining scenarios, process-based model output, monitoring data and the national lake assessment system. The results showed that the benefits of better land-use management were partly counteracted by future warming under these scenarios. Most importantly, the BN demonstrated the importance of including more biological indicators in the modelling of lake status: namely, that inclusion of cyanobacteria biomass can lower the ecological status compared to assessment by phytoplankton biomass alone. Thus, the BN approach can be a useful supplement to process-based models for water resource management. 1
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".