Safer Ships and Cleaner Seas: Reducing Vessel Risks through Targeted Inspections and Recognized Organization Oversight
Bibliographic record
Abstract
Abstract This article explores the lessons that might be learned from port State con-trol (PSC) inspections for flag States, examining the scholarly literature on vessel risk targeting. It begins by identifying the major classification socie-ties (and recognized organizations or ROs) before discussing the obligations and responsibilities of governments in achieving a global ‘safer ships and cleaner seas’ objective. It then presents a framework for thinking about the relationship between owner/operators, ship classification societies, flag State authorities and PSC parties in order to improve safety, discussing where gaps remain in the execution of the objective. The author draws con-clusions about where the future focus by flag States might achieve addi-tional improvements in their oversight of classification societies when they act as ROs, carrying out ship survey and certification functions on behalf of flag States. Identified challenges include improving the quality of data used in vessel risk targeting, expanding the transparency of that data for RO oversight, and focusing efforts on those flag States and ROs who do not meet their obligations.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".