Bibliographic record
Abstract
Bearing steel technology is a seemingly all-encompassing term to describe the metallurgical know-how on steels and processes for the production and usage of rolling bearing steels. In the pursuit of efficiency, the rolling bearing industry has standardized the steels and testing methods and reduced the costs of the metallurgical processes. As time elapses, the knowledge of why and how the standards were prepared fades into the past, i.e. it is forgotten. Much has been published in the open literature on the subject for specialists (fellow steel technologists) and the first ASTM International Symposium on Bearing Steel, sponsored by ASTM Committee A01 and its Subcommittee A01.28, was held in Boston in 1974. Since then, bearing steel symposia have been held at regular intervals and the program for the ASTM Eighth International Symposium on Bearing Steel, in Vancouver on May 21–22, 2009, contained papers on the subject of bearing steel technologies. In particular, the subject of micro cleanliness assessment methods in bearing steels was revisited 35 years after the 1974 Boston symposium on the subject.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.883 | 0.879 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".