Bibliographic record
Abstract
C. H. Bailey, University of Minnesota, USA (1924–1930) D. A. Coleman, U.S. Department of Agriculture, USA (1931–1938) M. J. Blish, Agricultural Experiment Station, Lincoln, NE, USA (1939–1943) W. F. Geddes, University of Minnesota, USA (1944–1962) K. A. Gilles, North Dakota State University, USA (1963–1967) W. G. Bechtel, American Institute of Baking, USA (1968–1977) P. E. Ramstad, American Maize Products Co., USA (1978–1984) Y. Pomeranz, Kansas State University, USA (1985–1992) J. J. Warthesen, University of Minnesota, USA (1993) V. F. Rasper, University of Guelph, Canada (1994–1999) J. M. Faubion, Kansas State University, USA (2000–2001) R. C. Hoseney, Kansas State University, USA (2002–2012) C. F. Morris, USDA-ARS-WWQL, USA (2013–2015) L. Copeland, University of Sydney, Australia (2016–current). One of my objectives going forward is to encourage the submission of more topical review articles. Hence, I invited a series of Centenary Reviews from eminent grain scientists; three of these distinguished reviews have already been published in Volume 100, and others will be published in subsequent issues. It is also noteworthy that among the many excellent papers published so far in Volume 100 of Cereal Chemistry, there are seven other reviews on the following topics: the chemistry of wheat gluten; contribution of glutenin and puroindoline proteins to wheat grain quality; advances in the development of multigrain bread; substitution of sugar with xylitol and maltitol in cakes; increasing whole grain intake within the Special Supplemental Nutrition Program for Women, Infants, and Children; and biotechnological approaches to improving aromas and flavors of legume-derived food products. I commend these excellent articles to you. I see my role as Editor-in-Chief as a facilitator, rather than a gatekeeper, for publishing high-quality research that connects with a broad readership. I look forward to reading submissions from your research and to making the experience of publishing in Cereal Chemistry rewarding and enjoyable. Open access publishing facilitated by The University of Sydney, as part of the Wiley—The University of Sydney agreement via the Council of Australian University Librarians.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.489 | 0.401 |
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".