Eficiencia del transporte férreo de carga Internacional: \nUn análisis a través de la envolvente de datos \n(Efficiency of railways international freight: \nan analysis with data envelopment)
Bibliographic record
Abstract
Abstract: 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 \ncurrent inputs and outputs as well as the target was carried out. The results shows that the \ncountries 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 \nand 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. \n \nResumen: 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 \nmultiplicación 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 \neficientes. En los resultados se observa que los países de China, Estados Unidos, Singapur y Tailandia muestran eficiencia técnica global; China, Estado Unidos, Hong Kong, Japón, Singapur y Tailandia tienen eficiencia técnica pura y los países de Brasil, Canadá, China, \nEstado Unidos, India. Rusia, Singapur y Tailandia poseen eficiencia a escala. Por lo que respecta a México, si bien no es eficiente acorde a los resultados mostrados existen diferentes propuestas y lineamientos a seguir donde se deberá dar prioridad a la diminución \nde los costos en un 62.67 por ciento hasta alcanzar el valor de 318.57USD; aumentar la infraestructura férrea y las vías electrificadas en un 85.88 por ciento para cada rubro.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".