Tecnologías post-cosecha y aseguramiento de la calidad en la comercialización de frutas y hortalizas frescas
Bibliographic record
Abstract
[SPA] Distintas tecnologías post-cosecha tienen gran importancia para reducir el deterioro de origen patológico y/o fisiológico y la consecuente pérdida de vida comercial que sufren los productos hortofrutícolas a lo largo de la cadena de comercialización. Establecemos el papel de estas tecnologías en el aseguramiento y consecución de la calidad, la importancia que tienen en mejorar la denominada “planificación de la calidad”, señalando como el éxito práctico e implantación de una tecnología también depende de su eficacia en la reducción de la variabilidad en el resultado entre las distintas partidas de producto sometidas a la misma. [ENG] There are several post-harvest technologies with great relevance reducing the pathological and/or physiological deterioration that takes place along the food chain in fresh fruits and vegetables. We establish here the role of these technologies in the quality assurance area, emphasizing both their role in the “planning for quality”, but also the importance that the technology must have reducing the variability in the result between the different lots of the fresh produce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".