Bibliographic record
Abstract
After the success of the first international Cake Symposium held in Leuven (Belgium) in 2012, the second international Cake Symposium (www.cakesymposium.org) was organized and took place August 25–26, 2014, in Boston, MA. The event was hosted by KU Leuven (Belgium) and UMass Amherst (United States) and sponsored by Puratos (www.puratos.com) and AACC International (www.aaccnet.org). Attendees (103 in total) from 8 countries (Belgium, Brazil, Canada, Japan, Mexico, Sweden, The Netherlands, and the United States) participated in this two day event covering different aspects of cake systems. Although cakes are very complex systems and have gained economic importance worldwide, not much is known about either ingredient functionality in cake systems or the chemical and physical processes that take place during cake making. Internationally recognized scientists with expertise in the complex world of cake making were invited to share their views and findings on different aspects of cake systems. The program for the first day focused on the functionality of different ingredients in cake systems. The symposium was officially opened by Organizing Committee Co-chairs Kristof Brijs (KU Leuven) and Amanda Kinchla (UMass Amherst). Second International Cake Symposium Held in Boston
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.131 | 0.044 |
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".