SPECIAL ISSUE ON INNOVATION AND APPLICATION OF MECHANICAL ENGINEERING GUEST EDITORIAL
Bibliographic record
Abstract
Machines make all industrial artifacts and mechanical products in the world. In this 21th century, people enjoy mechanical products that made lives well and convenient. However, creating a useful mechanical product mainly relies on academic development and technology innovation especially in the field of mechanical engineering. This special issue focuses on the innovation and application of mechanical engineering, which covers a wide range of fundamental studies, technical innovation, and industrial applications by applying the knowledge of mechanical-related approaches, processes and techniques. It presents 33 selected papers (Part 1) from the 3rd International Conference on Engineering and Technology Innovation (ICETI 2014) which was held from October 31 to November 4, 2014, in Kenting, Taiwan. These selected papers underwent a complete Transactions of the Canadian Society for Mechanical Engineering peer-review. Acceptance in this special issue was based on completion of the reviewers recommended and mandatory changes and final examination by the Guest Editor. We thank all of the authors who have submitted their work for publication in this special issue. We are also grateful to the many reviewers that provided their time and expertise in providing valuable peer review. Special thanks also go to Professor Paul J. Zsombor-Murray, Editor of Transactions of the Canadian Society for Mechanical Engineering, who had the foresight to support a special issue in this research area. This special issue is expected to be a core issue for academic researches and industrial engineers in the mechanical engineering field.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".