Bearing Steel Technologies: 10th Volume, Advances in Steel Technologies for Rolling Bearings
Bibliographic record
Abstract
Description You’ll gain unique insights on rolling bearing steel technologies with this collection of 31 papers from symposium presentations. This publication introduces revised ideas on bearing steel steelmaking and industries micro cleanliness specification requirements, rating methods and limits for re-melt bearing steels. The papers in this book, from the 10th ASTM International Symposium on Bearing Steel Technologies held in Toronto, Ontario, Canada, May 6-8, 2014 are a result of presentations under these section headings: – Advances in Bearing Steel Steelmaking and Processing– Steel Cleanliness Knowledge and Relationships with Rolling Bearing Functional Properties– New Bearing Steels for Improved Functional Properties– Softening and Hardening Heat Treatment Physical Metallurgy– Rolling Bearing Metallurgy for Wind Energy Applications– Developments in Fatigue and Rolling Contact Fatigue Testing This resource adds to the legacy of ASTM International’s support to the bearing steel industry, the latest Special Technical Publication in a long line of predecessors, between 1974 and 2013. Click the gray “Other Books in this Series” button above for a complete list.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.104 | 0.083 |
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".