Bibliographic record
Abstract
La presentacion de la comida es un ingrediente esencial en el ambito gastronomico, esto incluye creatividad, visualizacion, dibujo y color. Lo que significa que no solo son importantes las materias primas y la realizacion del plato, sino todo lo que le acompana a su presentacion global. El Food Densig es una tendencia gastronomica moderna, que se basa en destacar la figuras, las texturas, los colores y la imaginacion de chef al concebir y dar forma a la idea que nace de manera inspiradora de toda la secuencia de acontecimientos que da la experiencia al trabajar cotidianamente; con personas que deseen probar una nueva gama de alimentos pensados y creados para la degustacion y apreciacion de los comensales. Para el desarrollo de esta tematica se aplico una entrevista semiestructurada a tres chefs propietarios de locales gastronomicos de la Ciudad de Cuenca, con la finalidad de conocer si incorporan el food desing en sus negocios, lo que permitio llegar a la conclusion de que esta herramienta compuesta por diseno, arte grafico y culinario; esta siendo utilizada, ya sea por creatividad propia o adquirida a traves de su formacion, dando excelentes resultados en el crecimiento y atractivo de su negocio.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".