Bibliographic record
Abstract
Environmental concerns have prompted sulfur reductions in diesel fuels. These changes can decrease unadditized fuel lubricity, resulting in higher wear of diesel injection pumps and engines. Actual diesel pump and engine tests are costly, and existing diesel fuel lubricity bench tests appear to be failing to evaluate the fuel lubricity adequately. This has prompted the described development steps for the M-ROCLE bench test. It employs a crossed roller on cylinder geometry and computer data acquisition systems. The measured wear scar area stress is divided by the theoretical elastic Hertzian contact stress, and friction coefficient, to yield a dimensionless Lubricity Number (LN) indicating the lubricating property of the test fuel. Based on previous work and from correlation with HFRR test data, an LN > 1.0 was established as the pass value for a diesel fuel of sufficient lubricity. The overall coefficient of variation in published Lubricity Numbers, based on six individual test runs for some hundred fuels surveyed to date, was 5.3%. This is indicative of high precision in the M-ROCLE method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".