<b>FUSTT:</b> Forecasting Using Spatio-Temporal Transformers for Predicting Marine Stressors of Blue-Carbon Ecosystems
Bibliographic record
Abstract
Marine stressors such as temperature, salinity, turbidity, and chlorophyll strongly influence the health of Blue Carbon Ecosystems (BCEs) and play a critical role in global climate regulation. Accurate long-term forecasting of these stressors enables baseline construction, anomaly detection, and restoration planning. In this study, we presentFUSTT(Forecasting Using Spatio-Temporal Transformer), a deep learning model- derived from making efficient additions to the original Transformer architecture, specifically for multivariate long-term forecasting of marine parameters. FUSTT integrates a dual-encoder design, Dimensional Flipping Layer (DFL), and Flagged Dual Stage Attention (FDSA), enabling the model to capture complex temporal and cross-variable dependencies. Using real-world datasets collected by Ocean Networks Canada (ONC) at four representative Salish Sea locations (Discovery Passage, Burrard Inlet, Baynes Sound, and Boundary Pass), FUSTT consistently outperformed baseline models. It achieved 15–33% lower error than traditional transformer architectures and 30–40% higher accuracy compared to domain-specific models such as LSTM and CNN-LSTM, while also delivering faster training with minimal trade-offs. These results demonstrate FUSTT's capability to provide highly accurate and computationally efficient predictions of marine stressors at operational ecological monitoring sites, offering a practical foundation for advancing BCE management and climate resilience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".