DEVELOPING AN UNDERSTANDING OF “NEW GENERATION” EMULSIFIED FUELS
Bibliographic record
Abstract
ABSTRACT Previous research and development on emulsified fuels has essentially concentrated on the fate & behaviour, environmental impacts and response techniques for the specific trademarked fuel ORIMULSION®. However, it is recognised that this is just one example from the wider family of emulsified fuels, and they may not all necessarily behave in the same way due to differences in formulation/composition. A considerable database exists for this particular emulsion but as “new generation” emulsified fuels are developed and commercialised it will be necessary to gain an understanding of how similar (or different) they are in terms of their behaviour, potential impacts and appropriate mitigation measures. The current paper describes the typical characteristics of the emulsified fuel called MSAR™ (Multiphase Superfine Atomised Residue), and outlines the approach and early findings from an Environmental Studies Programme that has been initiated for this fuel. In order to provide a baseline and context this paper looks at similarities and differences compared to ORIMULSION® and Group V oils. Information is also provided on the general approach being adopted to better understand these emulsified fuels. Initial work investigating the likely fate and behaviour of MSAR™ spills has shown many similarities compared to ORIMULSION®. Both fuels are initially highly dispersible but, depending on the circumstances that prevail at the time of the spill, some coalescence can be expected to occur. Some potential differences are also highlighted between the two fuels, and the significance and implications will be taken into account in a planned on-going Environmental Studies Programme.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".