Applying Deep Learning to Predict Adverse Environmental Conditions in Fish Aquaculture Pens
Bibliographic record
Abstract
Aquaculture is critical for meeting the increasing global seafood demand as wild fish stocks decline. Effective management of aquaculture environments is vital for sustainability and operational efficiency. This study applies advanced deep learning models, specifically Temporal Fusion Transformers (TFT), to predict dissolved oxygen (DO) levels in fish pens. Accurate DO forecasting is essential for optimizing feeding schedules, enhancing fish health, and reducing environmental impact. Using extensive datasets from aquaculture sites in Canada and Scotland, our models were trained and validated to deliver reliable 24-hour DO predictions. Our phased approach, starting with single-pen data and scaling to multiple pens across various farms and regions, achieved mean absolute error (MAE) values below 0.7 for 24-hour forecasts. The findings demonstrate the potential of deep learning models in assisting real-time decision-making and mitigation strategies in aquaculture, fostering sustainable and efficient practices. Future work could incorporate additional environmental variables or farm inputs and extend model applications to diverse aquaculture 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 | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".