Analysis of model-based PM2.5 emission factors for on-road mobile sources in Mexico
Bibliographic record
Abstract
Se investigan los resultados del empleo del modelo US-EPA MOVES2010a para calcular los factores de emisión del parque vehicular mexicano, y se comparan dichos resultados con las estimaciones del Inventario Nacional de Emisiones de México (INEM) de 2005.El estudio muestra que los factores de emisión PM2.5 basados en modelos, actualizados a partir de estudios recientes, pueden tener un impacto significativo en la estimación de emisiones de PM2.5 procedentes de fuentes móviles en México.Porcentajes mayores de vehículos antiguos tienden a incrementar las estimaciones de emisiones PM2.5 cuando se utiliza el modelo MOVES2010a en comparación con las del INEM 2005; sin embargo, el impacto global sobre las emisiones de material particulado varía según la cantidad y antigüedad de los vehículos, y de acuerdo con los porcentajes de vehículos antiguos impulsados por diesel y gasolina en el parque vehicular de cada entidad federativa.Los resultados también indican que las estimaciones de PM2.5 con MOVES2010a fueron particularmente sensibles a la velocidad vehicular, la temperatura ambiente y el contenido de azufre, pero no a la humedad relativa.Hay una gran necesidad de comprender las características del material particulado fino emitido por vehículos impulsados por diesel o gasolina en las principales zonas urbanas de México.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".