Characterization of Low Sulfur Fuel Marine Fuel Oils (LSFO) - A new generation of marine fuel oils
Bibliographic record
Abstract
This is a summary report of the Multi-client project "Characterization of Low Sulfur Fuel Oils (LSFO) – A new generation of marine fuel oils" (2019-2020). The project has been funded by MPRI/Fisheries and Ocean-DFO Canada, ITOPF (R&D Award) and the Norwegian Coastal Administration (NCA). In order to meet new requirements for lower sulfur oxides (SOx) emissions to the air, new generation of low sulfur marine fuel oil (LSFO) are now replacing the traditional Intermediate fuels and heavy fuel oils (like IFO 180 and IFO 380) with "Ultra Low Sulfur Oils" – ULSFO (S ≤0.10 % m/m), for use in the Sulfur Emission Control Area (SECA) in Europe and North America from 2015, and a Global Sulfur Cap regulation was implemented from 2020 with "Very Low Sulfur Oils" (S ≤ 0.50 %m/m). This project aims to provide responders better knowledge and preparedness for spills involving new generation of low sulfur residual marine fuel oil on the market today. The project included laboratory studies with focus on fate and behaviour, potential toxicity and with relevance to the effectiveness of different oil spill response options (use of dispersants and in-situ burning). Test methodologies was also subjected to an interlaboratory study and experiments performed both in Norway (SINTEF) and in Canada (SL Ross) on one of the tested oils. The companies mentioned in this report provided samples for investigation of the fuel’s characteristics when spilled in sea water to help with the development of an industry response strategy for a new generation of low-sulfur fuel oils. Many of the low-sulfur fuels being developed by the industry share similar compositions, so it is important to notice that the findings of this report are not unique to the fuel samples analysed. The results of this study are indicative of a new generation of marine fuel oil across the wider industry. Further laboratory analysis of low-sulfur fuel oils from other suppliers is needed to give a clearer understanding of the characteristics and behaviours of individual products.
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How this classification was reachedexpand
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".