Navigating the blue frontier: A review of machine learning approaches for sustainable marine bioresource utilization
Bibliographic record
Abstract
The sustainable management and utilization of marine bioresources faces increasing challenges due to environmental variability, data scarcity, and the complexity of marine ecosystems. Addressing these issues demands advanced technological methods that enhance efficiency, precision, and environmental management. This review aims to examine how machine learning (ML) is transforming the field of marine bioresources by enabling precise species tracking, early detection of harmful algal blooms, rapid identification of bioactive compounds, and innovations in biofuels and sustainable fisheries. The novelty of this review lies in synthesizing recent developments in ML applications across these domains while critically analyzing emerging paradigms of hybrid and interpretable ML models. It highlights key algorithms, including artificial neural networks, random forests, gradient boosting, support vector machines, and adaptive neuro-fuzzy inference systems, emphasizing their potential to improve scalability and prediction performance. The review provides discussions on unresolved challenges, ethical integration pathways, and future directions for sustainable marine bioeconomy practices. Besides technological progress, the review highlights a governance and ethics perspective, emphasizing the need to align ML applications with ocean governance frameworks, environmental laws, and principles of social and ecological justice. By connecting technological innovation with institutional responsibility, this work provides a comprehensive roadmap for developing ML-driven systems that support rather than undermine ocean stewardship.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".