Further look at correlation between ASTM G65 rubber wheel abrasion and pin-on-disc wear tests for data conversion
Bibliographic record
Abstract
It is desirable that wear testing results obtained using different techniques can be mutually converted. In this study, wear rates of two groups of materials, i.e. cast iron and steel, were evaluated using the ASTM G65 dry sand rubber wheel abrasion and pin-on-disc wear testers respectively. The conversion between results obtained using the two different methods was investigated. It was shown that the two sets of wear data can be mutually converted, following a linear relation. The slope and position of the line that fits the data are, however, affected by Young’s modulus of the materials and the applied load. Such effects are attributed to the fact that the wear rate of the materials measured using the G65 method was influenced by not only hardness but also elastic modulus, while for the pin-on-disc tests, the wear rate was more dominated by hardness of the target materials especially under larger applied loads. Relevant mechanisms are discussed with a further look at the Archard’s wear equation.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".