Bibliographic record
Abstract
Cindy M. Stewart, PhD, is the founder and principal of Innovative Food Science Consulting (IFSC), which provides consulting and advisory services to the biotech, food, and food ingredients industry. Prior to founding IFSC, she was the Vice President of Open Innovation in Corbion where she strategically led the new global Open Innovation business model and team. Prior to joining Corbion, Cindy was the Global R&D Leader for Cultures, Food Protection and Food Enzymes in the IFF Health & Biosciences Division (formerly DuPont Nutrition & Biosciences). Other previous positions held include Senior Director of Advanced Research at PepsiCo; General Manager, Silliker, Inc Food Science Center; Director, Scientific Affairs, National Center for Food Safety and Technology; High Pressure Processing Program Manager for CSIRO's Food Science Australia; Senior Research Microbiologist, Nabisco; and Research Associate II, University of Delaware. Dr Stewart's expertise as a food scientist is recognized globally, as she has published and presented over 125 papers and book chapters on nonthermal processing technologies, predictive microbiological modeling, and microbial risk management. Cindy served on the IFT Board of Directors and was the 78th IFT President, 2017-2018. In 2020, she was elected as an IFT Fellow. Cindy is a member of the Tuskegee University Food and Nutritional Sciences Advisory Board, serves on the Delaware Hospice Board of Trustees, and is an International Food Information Service Corporate Advisory Board Member.She holds BS and MS degrees in Food Science from the University of Delaware and a PhD in Food Science from Rutgers University. The author has no conflicts of interest to disclose. Correspondence: Cynthia M. Stewart, PhD, Innovative Food Science Consulting, 11 Perth Dr, Wilmington, DE 19803 ([email protected]).
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.075 | 0.058 |
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".