Bibliographic record
Abstract
The Editor and Editorial Board of the Journal of Manufacturing Science and Engineering would like to thank all of the reviewers for volunteering their expertise and time reviewing manuscripts in 2019. Serving as reviewers for the journal is a critical service necessary to maintain the quality of our publication and to provide the authors with a valuable peer review of their work. Below is a complete list of reviewers for 2019. We would also like to acknowledge four outstanding Reviewers of the Year.2019 Reviewer of the YearMark Jackson—Kansas State University, USAJianfeng Ma—Saint Louis University, USAChris Arcona—Saint-Gobain High Performance Materials, USAOguzhan Tuysuz—Pratt & Whitney Canada, CanadaThe Reviewers of the Year Award is given to reviewers who have made an outstanding contribution to the journal in terms of the quantity, quality, and turnaround time of reviews completed during the past 12 months. The prize includes a Wall Plaque, 50 free downloads from the ASME Digital Collection, and a one year free subscription to the journal.
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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 | 0.033 | 0.320 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.072 | 0.065 |
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".