Large-scale oil spills and flag-use within the global tanker fleet
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
SUMMARY Within the global oil shipping sector, flag states that inadequately fulfil obligations to effectively exert jurisdiction over vessels flying their flags have been criticized for facilitating the existence of substandard ships. This paper examines the topic of flag-use and its potential association with oil spill risk. Flags most associated with accidental oil spills were identified through comparing the flag composition of the global oil tanker fleet with that of vessels that have been involved in the 100 largest tanker spills on record. Vessels flying flags of states that have exhibited consistent patterns of failure in compliance with international obligations, defined here as ‘flags of non-compliance’ (FoNCs), were found to be significantly more common amongst the vessels that have been involved in spill incidents. However, this was dependent on how the Liberian flag was qualified throughout the time period considered. If measures are being sought to reduce the risk of tanker involvement in large-scale oil spills further, vessel owners should be deterred from registering with FoNCs that are highly accessible to foreign owners, and political measures should be taken to put pressure on flag states that operate all other FoNCs to improve effective jurisdiction over ships flying these flags.
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 | 0.001 | 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 | 0.001 | 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