The Impact of E85 Use on Lubricant Performance
Bibliographic record
Abstract
Ethanol is widely used as a gasoline component to provide a prescribed amount of oxygenates and for its perceived advantages of less dependence on petroleum based products and lowering overall CO2 emissions. In most cases the level of ethanol in gasoline does not exceed 10%. In some parts of the Unites States, E85 fuel consisting of 85% ethanol and 15% gasoline is commonly available. Many US vehicles sold today are specially adapted for use of both gasoline and high ethanol fuels; so-called Flexible Fuel Vehicles (FFV). While high ethanol fuels are currently a small percentage of the overall gasoline pool, they provide an interesting opportunity to study the effects that ethanol use in gasoline may have on lubricant related performance. Based on past industry experience with methanol based fuel, theoretical areas of concern for ethanol based fuels are valve train rust and potential problems associated with high amounts of water in the lubricant. The authors have studied the lubricant related effects of E85 use in well controlled field testing under cold climate conditions. It is concluded that the use of E85 fuel leads to significantly higher water levels in the lubricant as compared to the use of gasoline. The use of E85 does not promote the formation of valve train rust in FFV engines run on modern lubricants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".