Fase 6. Presentar y sustentar Proyecto Final. Logística de distribución de productos perecederos: estudio de caso:Fuente de Oro (Meta) y Viotá (Cundinamarca)”.
Bibliographic record
Abstract
Figura 1. Red estructural Proceso de Mercados Campesinos. Figura 2. Diagrama de flujo por responsabilidades de los procesos Mercados Campesinos. Figura 3. Modelo Scor. Figura 4. Indicadores en la cadena de suministro y logística. Figura 5. Modelo Score PMC – Canal Mayorista. Figura 6. Modelo Score PMC – Canal presencial. Figura 7. Modelo Scor. Figura 8. Indicadores requeridos en la red estructural. Figura 9. Cadena de valor de Porter Sector Agroindustrial. Figura 10. Desarrollo de las políticas clúster en Colombia. Figura 11. Elementos principales para un enfoque de clúster basado en el cambio estratégico. Figura 12. Identificacion de Clústeres. Figura 13. Capacidad de Refrigeración en los países – año 2008. Figura 14. Flota con refrigeración, distribución por configuración de equipos, 2005, porcentajes. Figura 15. Tipos de empaque. Figura 16. Trámite para exportaciones en Canada. Figura 17. Cadena de suministro de productos. Figura 18. Canales de comercialización. Figura 19. Pasos a seguir para ejecutar la hoja de ruta. Tabla 1. Ejemplo de indicadores de desempeño de nivel superior. Tabla 2. Modelo Scor – Nivel 1. Tabla 3. Modelo Scor – Indicadores por procesos. Tabla 4. Indicadores modelo SCOR. Tabla 5. Ejemplo de indicadores de desempeño de nivel superior. Tabla 6. Temperaturas requeridas para el almacenamiento de productos. Tabla 7. Benchmark Logística de perecederos en Colombia – Caso de estudio. Fuente de Oro y Viota. Tabla 8. Hoja de ruta.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.198 | 0.047 |
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".