Actions at port are essential for ending illegal, unreported and unregulated fishing
Bibliographic record
Abstract
The Port State Measures Agreement (PSMA) provides a legally-binding mechanism to deter illegal, unreported and unregulated (IUU) fishing by foreign vessels through standardized reporting, inspections, information sharing, and port denial. To be more effective, region-wide 25 adoption and consistent implementation of PSMA are essential for ensuring IUU fishing vessels cannot easily land catches with identities that will receive less scrutiny or in locations with weaker governance. The PSMA also recognizes the centrality of tackling IUU fishing in domestic fleets, which account for more than 90% of port visits. Port State measures aligned with PSMA need to be applied to these fleets to prevent the flag-switching that allows vessels to 30 dodge oversight. Accelerating adoption and implementation of PSMA as well as extending port State measures to domestic fleets is crucial for reducing opportunities to hide illegal catches and maximize the potential of actions at port to address IUU fishing. One-Sentence Summary: Reducing IUU fishing risks will depend on consistent, regional 35 implementation of effective port State measures across both foreign and domestic fleets.
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.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".