Empowering high seas governance with satellite vessel tracking data
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
Abstract Between 1950 and 1989, marine fisheries catch in the open‐ocean and deep‐sea beyond 200 nautical miles from shore increased by a factor of more than 10. While high seas catches have since plateaued, fishing effort continues to increase linearly. The combination of increasing effort and illegal, unreported and unregulated (IUU) fishing has led to overfishing of target stocks and declines in biodiversity. To improve management, there have been numerous calls to increase monitoring, control and surveillance (MCS). However, MCS has been unevenly implemented, undermining efforts to sustainably use high seas and straddling stocks and protect associated species and ecosystems. The United Nations General Assembly is currently negotiating a new international treaty for the conservation and sustainable use of biodiversity beyond national jurisdiction (BBNJ). The new treaty offers an excellent opportunity to address discrepancies in how MCS is applied across regional fisheries management organizations ( RFMO s). This paper identifies ways that automatic identification system ( AIS ) data can inform MCS on the high seas and thereby enhance conservation and management of biodiversity beyond national jurisdictions. AIS data can be used to (i) identify gaps in governance to underpin the importance of a holistic scope for the new agreement; (ii) monitor area‐based management tools; and (iii) increase the capacity of countries and RFMO s to manage via the technology transfer. Any new BBNJ treaty should emphasize MCS and the role of electronic monitoring including the use of AIS data, as well as government–industry–civil society partnerships to ensure critically important technology transfer and capacity building.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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