Bibliographic record
Abstract
A number of laboratory-scale test methods are available to predict the effects of fuel lubricity on injection system wear. Anecdotal evidence exists to indicate that these methods produce poor correlation with pump wear, particularly for fuels that contain lubricity additives. The issue is further complicated by variations in the lubricity requirements of full-scale equipment and the test methodologies used to evaluate the pumps. However, the cost of performing full-scale equipment testing severely limits the quantity of data available for validation of the laboratory procedures at any single location. In the present study, the technical literature was reviewed and all previously published data was combined to form a single database of 175 pump stand results. This volume of data allows far more accurate statistical analysis than is possible with tests performed at a single location. The results indicate differences in the effectiveness of the standardized laboratory-scale methods. The High Frequency Reciprocating Rig (HFRR) produced much lower correlation with pump wear than did the Scuffing Load Ball on Cylinder Lubricity Evaluator (SLBOCLE), with HFRR tests performed at 60°C being even less accurate than those performed at 25°C. Correlation was also lower for fuels that contain lubricity additives as opposed to neat fuels. Multi-variable regression analysis of the data indicates that correlation with injection equipment is improved by combining the results from different laboratory-scale test procedures using simple mathematical formulae. The squared correlation (R2) of laboratory-scale wear tests with pump wear rating is unlikely to greatly exceed 77%, due to the inherent variability of the pump data. Combination of three or more laboratory tests using the equations derived from the multi-variable regression analysis allowed this maximum value to be achieved. As a result, it is hoped that the equations derived in the present paper may become more widely used to better predict full-scale pump wear.
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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 | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.046 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".