Investigation of Apple Jelly" Contaminant in Military Jet Fuel"
Bibliographic record
Abstract
Between 1980 and 1985, a representative of Imperial Oil made a presentation to Subcommittee J (aviation fuels) of ASTM Committee D2 concerning a contaminant found in the Alberta Products Pipeline (APPL). The contaminant had a high viscosity and was eventually called "APPL" jelly. It is not clear whether the name eventually evolved into apple jelly or someone coined the name separately because of the appearance of the contaminant. However, since that time, the name has been applied to a range of contaminants found in aviation fuel delivery systems (primarily U.S. Air Force). The objective of this project was to characterize this aviation fuel contaminant known with respect to the compositional and process conditions required for its formation, and to determine possible methods, both compositional and process, whereby its formation can be reduced or prevented. This work has demonstrated that apple jelly is a complex mixture. It begins with water and DiEGME (diethylene glycol monomethyl ether). This mixture reacts with its environment, extracting and dissolving compounds from the materials with which it comes in contact. In this work we started with apple jelly samples collected throughout the DoD/Air Force fuel-distribution system. The majority of our samples came from fuel systems delivering JP-8 to aircraft. All the fuels contained corrosion inhibitor, FSII (fuel system icing inhibitor), and SDA (static dissipator additive) in varying amounts. Other than FSII, this work focused on only one JP-8 additive, SDA. The work presented in this report explains the majority of the properties of the various apple jelly samples we received. We were able to demonstrate how thin and thick apple jelly, of the types we analyzed, could form.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".