Advances in forecasting of harmful algal blooms in freshwater ecosystems
Bibliographic record
Abstract
Aquatic scientists are being increasingly challenged to better predict harmful algal blooms (HABs), given the accelerating rate of global eutrophication. Forecasting of HABs has undergone substantial transformations, given the need for better management practices and rising concern over the negative impacts from HABs. This review presents a chronological examination of HAB forecasting methods, tracing the field's evolution from early statistical models to the current application of convolutional neural networks, deep learning, and multi-model integration. Initially dominated by conventional statistical approaches, the field of predicting HABs has shifted towards models better able to capture the complex, nonlinear dynamics of HABs. With growing public concern and environmental awareness, recent application of models emphasise the importance of understanding the biological and environmental conditions that lead to HAB formation in their specific environments. Emerging trends include the hybridisation of models, increased use of unsupervised deep learning, and the incorporation of individual-based models to account for ecological heterogeneity. As HAB forecasting continues to evolve in response to climate change and technological progress, the integration of advanced computational methods, real-time data, and stakeholder collaboration stands out as essential for building more accurate, efficient, and adaptive forecasting systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".