Modelo de aproximación lineal para la medición de resiliencia en cadenas de suministro
Bibliographic record
Abstract
RESUMENLa identificación del nivel de resiliencia de un sistema es fundamental para la toma de decisiones en la prevención y planeación de estrategias de recuperación ante fallas que lo puedan afectar.Este artículo busca diseñar una métrica de aproximación lineal para la medición de resiliencia en cadenas de suministro ante eventos disruptivos inesperados.Las métricas tradicionales de resiliencia simplifican el comportamiento de un sistema después de la ocurrencia de una falla, dificultando que las mediciones se puedan realizar en escenarios de fallas de mayor complejidad.Una métrica general de resiliencia es desarrollada y validada en un caso simulado de una cadena de suministro de dos eslabones con interrupciones en el proceso de transporte.Los resultados confirman que las métricas de resiliencia tradicionales sobreestiman los niveles de resiliencia del sistema, debido a la inhabilidad de modelar diferentes escenarios de eventos disruptivos.Los resultados del caso simulado sugieren que la nueva métrica mejora la estimación de resiliencia en comparación con las métricas lineales previas y, adicionalmente provee la flexibilidad necesaria para ser utilizada en otros tipos de sistema diferentes a cadenas de suministro.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".