Bibliographic record
Abstract
With this first 2007 issue of the Journal, it is time again to recognize the services of all individuals who have contributed to its continuing growth in the past year. I would like to gratefully acknowledge the support of the increasing number of authors who have chosen this Journal over others, and also the dedication and work of all reviewers and members of the Editorial Board. Particularly deserving of special appreciation and thanks are the outstanding reviewers and two of the retiring Associate Editors for their six years of exceptional service.On behalf of the members of the Editorial Board and staff, I would like to wish all a prosperous New Year.K. F. EhmannEach year, based on nominations by the members of the Editorial Board, the services of outstanding reviewers are recognized. This year, they are:Yusuf AltintasUniversity of British ColumbiaMuammer KocVirginia Commonwealth UniversityYuefeng LuoFederal Mogul CorporationSteven MalkinUniversity of MassachusettsGracious NgaileNorth Carolina State UniversityO. Bruak OzdoganlarCarnegie Mellon UniversityWon-Soo YunKorea Polytechnic UniversityAlbert Shih is a Professor of Mechanical Engineering, University of Michigan at Ann Arbor. From 1991 to 1998, Dr. Shih worked at Cummins Inc. in Columbus, Indiana as a manufacturing engineer to develop advanced engineering materials for a wide variety of diesel engines and fuel systems applications. From 1998 to 2002, he was an Associate Professor in the Department of Mechanical and Aerospace Engineering at North Carolina State University at Raleigh, North Carolina. Dr. Shih's research and teaching interests are in precision machining of advanced materials, biomedical manufacturing, precision machine design, optical measurements, and electrical discharge machining (EDM). Professor Shih is the recipient of the 1999 ASME BOSS Award, 2000 NSF CAREER Award, and 2004 SAE Ralph Teetor Education Award. Dr. Shih also serves as an Associate Editor of the International Journal for Manufacturing Science and Production.Y. Lawrence Yao is Professor and Chair of the Department of Mechanical Engineering at Columbia University, where he also serves as the Director of the Manufacturing Research Laboratory (MRL). Before joining Columbia in 1994, he was a Senior Lecturer in the School of Mechanical and Manufacturing Engineering at the University of New South Wales, Sydney, Australia. He received his Ph.D. from the University of Wisconsin–Madison in 1988, following his MS from the same institution, and a BE from Shanghai Jiao Tong University, China, all in Mechanical Engineering. Dr. Yao and his team in the Manufacturing Research Laboratory (MRL) are interested in multidisciplinary research in manufacturing and design, nontraditional manufacturing, laser materials processing, laser assisted material removal, shaping, and surface modification, laser applications in industry and art restoration, robotics in industry, and health care. Professor Yao currently serves on the Board of Directors of the Laser Institute of America, and of the North American Manufacturing Research Institution of SME. He also serves as an Associate Editor of the SME Journal of Manufacturing Processes, SME Journal of Manufacturing Systems, and High Temperature Material Processes: An International Journal, France. He is the recipient of the 2006 Blackall Machine Tool and Gauge Award from ASME.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.196 | 0.147 |
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".