Characterization of Low Sulfur Fuel Oils (LSFO) – A new generation of marine fuel oils - OC2020 A-050
Bibliographic record
Abstract
This Multi-client project "Characterization of Low Sulfur Fuel Oils (LSFO) - A new generation of marine fuel oils" has been a 1-year project (2019-2020). The project has been funded by MPRI/DFO Canada, ITOPF and the Norwegian Coastal Administration. In order to meet new requirements for lower sulfur oxides (SOx) emissions to the air, new generation of low sulfur marine fuels are now replacing the traditional Intermediate bunker fuels and heavy fuel oils (like IFO 180 and IFO 380) with "Ultra Low Sulfur Oils" – ULSFO (S < 0.1 % 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 < 0.5 %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 on these LSFO oils 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 were performed both in Norway (SINTEF) and in Canada (SL Ross) on one of the tested oils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".