Cold Starting and Pumpability Studies in Modern Engines — Results from the ASTM D02.07C Low Temperature Engine Performance Task Force Activities: Test Oil Selection and Rheological Analysis
Bibliographic record
Abstract
Two series of test oils were obtained for the Low Temperature Engine Performance (LTEP) task force activities: (a) SAE 0W-30, 5W-30, 10W-30, 15W-40, 20W-50 and 25W-30 grades were fully on-specification commercial oils (LTEP 1–7), used in cold starting and Phase I pumpability studies, (b) experimental test oils for Phase II pumpability work (LTEP 20 series) either did not meet the then-current SAE J300 pumping viscosity (TP-1 MRV) limits, or showed significant D5133 gelation index values, and included nominal SAE 5W-30, 10W-30 and 15W-40 grades. LTEP participating labs provided rheological data on the oils including measurements by D 445, D 3829, D 4684, D 5133, and D 5293. Multi-temperature MRV and CCS measurements allowed correlation to the exact engine test temperatures by fitting viscosity and yield stress data to equations. The functional form of the equations is presented and explained, and the fitted coefficients are tabulated. Gelation index and gelation index temperature values, were simply averaged for each oil. For the D 3829 and D 4684 methods that measure yield stress in addition to viscosity, an adjustment was made to the measured viscosity before fitting, to allow for yield stress effects.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".