Special Issue on Production Technologies at the End of the First Quarter of the 21st Century
Bibliographic record
Abstract
Social issues, such as global environmental problems and worker shortages, are becoming more apparent, and society’s values are changing rapidly. The same issues occur in production and processing technologies, which are required not only to improve the functionality and reliability of manufactured products but also to satisfy various requirements, such as information technology, intelligence, and energy saving, in addition to conventional high-speed and high-precision technology. In this regard, various research and development activities are being conducted, which include new machining technologies such as additive manufacturing, technologies for monitoring machine conditions, and technologies for collaborative systems between operators, robots, and machines. This special issue encompasses various topics related to the current production and processing technologies, with this year signifying the end of the first quarter of the 21st century. This issue contains 18 papers that focus on measurement technologies for evaluating motion accuracy, process-planning technology, tool-path generation for five-axis machining, cutting technologies, and automation technologies. The editors sincerely thank all authors for their dedication and high-quality papers. Additionally, we would like to thank all the reviewers for their efforts in ensuring the high quality of this issue. Finally, we sincerely hope that the papers pertaining to machine tools and related manufacturing technologies will contribute to the development of the global society.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.027 |
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".