Eficiencia del transporte férreo de carga Internacional: Un análisis a través de la envolvente de datos
Bibliographic record
Abstract
Keywords: data envelopment, economic efficiency, rail freightAbstract: In this paper based on the methodology of Analysis of Data Envelopment (DEA), the efficiency is calculated 32 countries with the largest trade flows for 2013. During the first stage, the overall technical efficiency is determined, which is calculating the product of pure technical efficiency and the efficiency of scale. In a second, benchmarking analysis given current inputs and outputs as well as the target was carried out. The results shows that the countries of China, United States, Singapore and Thailand show overall technical efficiency; China, United States, Hong Kong, Japan, Singapore and Thailand have pure technical efficiency and the countries of Brazil, Canada, China, United States, India. Russia, Singapore and Thailand have scale efficiency. With respect to Mexico, although it is not efficient according to the results shown there are different proposals and guidelines to follow which should give priority to the decrease of costs by 62.67 percent to the value of 318.57USD; increase rail infrastructure and pathways electrified by 85.88 percent for each item.Palabras clave: eficiencia económica, Envolvente de Datos, transporte férreo de cargaResumen: En este trabajo a partir de la metodología del Análisis de la Envolvente de Datos (DEA), se calcula la eficiencia 32 países con mayor flujo de comercio para el año 2013. en una primera etapa, se determina la eficiencia técnica global, la cual es el producto de la multiplicación de la eficiencia técnica pura entre la eficiencia de escala. en un segundo apartado, se realizó un análisis de benchmarking considerando los inputs y outputs actuales de tal forma que derivado del estudio se puedan dar estrategias a seguir para los casos no eficientes. En los resultados se observa que los países de China, Estados Unidos, Singapur
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".