Extending SAE J300 to Viscosity Grades below SAE 20
Bibliographic record
Abstract
The SAE Engine Oil Viscosity Classification (EOVC) Task Force has been gathering data in consideration of extending SAE J300 to include engine oils with high temperature, high shear rate (HTHS) viscosity below the current minimum of 2.6 mPa⋅s for the SAE 20 grade. The driving force for doing so is fuel economy, although it is widely recognized that hardware durability can suffer if HTHS viscosity is too low. Several Japanese OEMs have expressed interest in revising SAE J300 to allow official designation of an engine oil viscosity category with HTHS viscosity below 2.6 mPa⋅s to enable the development of ultra-low-friction engines in the future. This paper summarizes the work of the SAE EOVC Low Viscosity Grade Working Group comprising members from OEMs, oil companies, additive companies and instrument manufacturers to explore adoption of one or more new viscosity grades. Past studies relating HTHS viscosity to engine wear will be reviewed, and an analysis of the rheological properties of commercial SAE xW-20 oils will be presented. New data will also be offered to demonstrate the feasibility of formulating engine lubricants to meet HTHS viscosities as low as 1.7 mPa⋅s while meeting current low-temperature oil starting viscosity and ILSAC volatility requirements. Finally, the paper will address the measurement precision of current HTHS methods on prototype low HTHS engine 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.020 |
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".