Machine learning-based prediction of jellyfish blooms and their influence on coastal fisheries
Bibliographic record
Abstract
Jellyfish blooms predictably exacerbate the economic and ecological challenges coastal fisheries face globally. Effective fishery management relies heavily on predicting growth patterns alongside mitigating possible risks. This investigation initiates a framework utilizing machine learning to forecast the growth of jellyfish populations and their corresponding impact on coastal fisheries. The described system, JellyNet, is a convolutional neural network (CNN) that utilizes high-resolution remote-sensing satellite imagery captured by drones (UAVs). Jelly Net allows fisheries to act based on predictions, providing 6 to 8 hours of early detection and bloom event forecasting. A dataset derived from Croabh Haven, UK, and Pruth Bay, Canada, with 1,539 images, was annotated into two categories: 'Bloom present' and 'No bloom present,' which is essential for precise feature identification during bloom detection. Employing transfer learning featuring the VGG-16 architecture, JellyNet surpassed baseline models, achieving a pinnacle accuracy of 97.5%. Furthermore, the study analyzes the relationship between predicted bloom occurrences and subsequent changes in fish catch data, illustrating jellyfish blooms’ dominantly negative influence on productivity. This study reveals the mastery machine learning holds in predictive analysis and sustainable coastal fishery operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".