Safer Ships and Cleaner Seas: Reducing Vessel Risks through Targeted Inspections and Recognized Organization Oversight
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 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 RO s) 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 RO s, 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 RO s who do not meet their obligations.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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