A Review of the Problems Posed By Spills of Heavy Fuel Oils
Bibliographic record
Abstract
ABSTRACT Experience shows that spills of persistent heavy fuel oils, whether from cargo carried on tankers or bunker fuel used by ships in general, are among the most difficult to combat. Because of their viscous nature, which leads to prolonged persistence in the marine environment, these oils have the potential to cause widespread contamination of sensitive environmental and economic resources. This is also true for heavy crude oils and those crudes that form viscous and persistent emulsions, and many of the observations contained in this paper apply equally to such oils. The paper highlights some of the specific problems that the International Tanker Owners Pollution Federation Limited (ITOPF) staff have experienced during their on-site involvement in over 150 fuel oil spills during the last 25 years including incidents such as the Eleni V (United Kingdom/Netherlands, 1978), Tanio (France, 1980), Nestucca (United States/Canada, 1988), Korea Hope (South Korea, 1990), Vista Bella (Caribbean, 1991), Katina P (Mozambique, 1992), Morris J Berman (Puerto Rico, 1994), Apollo Sea (South Africa), Iron Baron (Australia, 1995), Nakhodka (Japan, 1997), Evoikos (Singapore, 1997), Kure (United States, 1997), New Carissa (United States, 1999), Erika (France, 1999), Volgoneft 248 (Turkey, 1999), and Treasure (South Africa, 2000). This review of the practical lessons that can be learned from past events is intended to provide an informed basis for the selection of more effective response techniques and equipment, and for the development of improved spill response management and contingency planning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".