Assessment of Correlation Between Bench Wear Test Results and Engine Cylinder Wear, Short-Trip Service
Bibliographic record
Abstract
<div class="htmlview paragraph">Bench tests are often less expensive and faster than vehicle tests. However, correlation between bench tests and the engine needs to be proven, otherwise bench tests may be misleading. This investigation explored the relationships between bench wear test results and engine results from short-trip driving tests for a variety of conditions: fresh vs. used oil, different methods for assessing wear, and chemical effects such as oil contamination and differences in the fuel. There was a negative correlation between bench tests with fresh oil compared to vehicle test results with used oil, which suggests that bench wear characteristics of fresh engine oil should not be used to determine engine wear rates under the conditions tested here. Statistical analysis of bench test wear rates with used engine oil, compared to engine wear measurements, indicated that the trends were in an appropriate direction, with some scatter in the results. Visual assessment of bench wear surfaces indicated significant differences between one lubricant and another.</div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".