The use of emerging autonomous technologies for ocean monitoring: insights and legal challenges
Bibliographic record
Abstract
The critical role of biology Essential Ocean Variables (EOVs) in advancing our understanding of marine ecosystems underscores the need for sophisticated observation tools like Autonomous Underwater Vehicles (AUVs), Unmanned Aerial Vehicles (UAVs), Maritime Autonomous Vehicles (MAVs). However, the integration of these technologies in Marine Scientific Research (MSR) has surfaced significant legal and policy challenges. This study, informed by insights from forty-six experts across academia, oceanographic institutions, industry, and intergovernmental organizations, identifies six principal legal challenges relevant to the: operation and navigation of AUVs, data collection, security, environmental impact, animal tagging, and intellectual property rights. Effectively addressing these challenges requires a coordinated, multi-stakeholder approach among the scientific community, policymakers, and international bodies. States may promote an initiative to drive progress in ocean observation while laying the groundwork for advancements. To address the operational and regulatory complexities, States may coordinate collaboration through involvement of the Intergovernmental Oceanographic Commission (IOC), the International Maritime Organization (IMO), and the World Meteorological Organization (WMO), for example. Additionally, coordination with frameworks such as the BBNJ Agreement, UNCLOS, the Convention on Biological Diversity’s Kunming-Montreal Global Biodiversity Framework (CBD KM-GBF), and regional organizations like the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) would ensure a comprehensive and inclusive approach.
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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 | 0.035 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| 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".