Understanding Harmful Algal Bloom Dynamics in a Mediterranean Hypereutrophic Reservoir insights from a Bayesian Network and a Structural Equation Model
Bibliographic record
Abstract
Cyanobacteria blooms, especially those involving Microcystis, are an increasing problem facing many freshwater systems worldwide. In this study, a Bayesian Network (BN) along with a Structural Equation Model (SEM) were concurrently developed through data-driven learning and expert elicitation in order to better delineate the main pathways responsible for the Microcystis dominance in a Mediterranean semi-arid hypereutrophic reservoir. The resulting two model structures were then compared with regards to the pathways they identified between the physical lake conditions and the nutrient loads on one hand and Microcystis dominance on the other. The two models were also used to predict the probability of bloom formation under different scenarios of climate change and nutrient loading. Both models showed that, given the eutrophic status of the study reservoir, direct temperature effects appear to be the primary driving force behind the Microcystis growth and dominance. Indirect temperature effects, which modulated water column stratification and internal nutrient release, were also found to play an important role in bloom formation. On the other hand, both models revealed that the direct nutrient pathways were less important as compared to the temperature effects, with internal nutrient loads dominating over external loads due to the seasonal variability in river flows, typical of Mediterranean rivers. Nevertheless, the BN model was unable to capture the recursive relationships between Microcystis and its forcings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".