Special Issue on High Performance Cutting and Related Manufacturing Technologies
Bibliographic record
Abstract
The 4th CIRP International Conference on High Performance Cutting had been held at the Nagaragawa Convention Center in Gifu City of Japan in October 2010. The scope of the conference was to review and discuss the visions, state of the art and innovations in the area of high performance cutting and related manufacturing technologies. This conference is originated from the CIRP Working Group in High Performance Cutting established by Professor G. Byrne in 2001. After four workshops in Europe, the 1st international conference on HPC was held in Aachen in 2004 chaired by Professor Byrne and Professor F. Klocke. The second one was held in Vancouver under the chairmanship of Professor Y. Altintas and the 3rd one was organized by Professor Byrne and Dr. O’connell in Dublin. In this conference, 144 interesting papers were presented from 19 countries. The editorial committee of IJAT selected the excellent papers presented at the conference and requested the authors to contribute manuscripts in expanded version of conference papers. As a result, 25 papers were accepted for the publication. I believe that this special issue provides the readers valuable information at the leading edge of manufacturing technologies. I would like to express my sincere appreciation to all of the authors and reviewers for their invaluable effort.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.038 |
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".