Propuesta de implementación de un Sistema Just-In-Time (JIT) en el proceso de Almacenamiento en Frío para Mejorar la Eficiencia y Calidad en una Empacadora de Camarones
Bibliographic record
Abstract
En el almacenamiento en frío de camarones, los tiempos de espera afectan la calidad, frescura y textura del producto, además de aumentar los costos operativos. Este proyecto propone implementar un sistema Just-In-Time (JIT) en el área IQF antes del almacenamiento, analizando procesos actuales y eliminando cuellos de botella mediante herramientas como el ciclo de Deming. Se busca mejorar la eficiencia operativa, reduciendo tiempos de espera en un 30%, tiempos muertos en un 25% y la tasa de productos rechazados en un 20%. Además, se implementará un control de calidad en temperatura para garantizar condiciones óptimas, aplicando técnicas de mejora continua en la industria acuícola.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".