Navigating the blue frontier: A review of machine learning approaches for sustainable marine bioresource utilization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it