The plating manifesto (I): from decoration to creation
Bibliographic record
Abstract
At a time when a growing number of chefs and innovative food industries are starting to set up their own research kitchens and work with renowned scientists, it is surprising to see that issues related to the visual presentation of food on the plate are being left out of these successful exchanges. The variety of presentations created by chefs, and the number of varieties of tableware now available to achieve them, represent a formidable opportunity for cognitive scientists to study the more complex effects of vision on food experiences, which certainly should not be missed. Chefs can also benefit from the new insights that a scientific approach can bring to these areas, which previously have often been left to intuition. In this manifesto, we claim that this transfer of knowledge represents much more than merely another addition to the art and science of cuisine: it is its essential completion, as gastronomy moves more and more toward the ideal of a total multisensory art, as captivating for the eye as it is for the palate. Before turning to the scientific recommendations and review in the second part of our manifesto, we want to promote a different approach to plating, which breaks with the more functional and decorative purposes of plate ware, and puts experiments in visual presentation at the heart of modernist culinary expression.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".