Remote sensing study of motor vehicles’ emissions in Mexican Cities
Bibliographic record
Abstract
According to the North American Free Trade Agreement (NAFTA), signed by Canada, the USA and Mexico in 1992, beginning on January 1 st 2009, Mexico may not maintain restrictions on the importation of used cars older than ten years.The increased influx of used vehicles might cause significant changes in the composition of the country's vehicle fleet and this might increase its contribution to air emissions.Due to this situation and the lack of reliable information for decision-making, in 2007 the National Institute of Ecology of Mexico carried out studies of emissions, activity and composition of the vehicular fleet in the Mexican cities of Mexicali and Tijuana, in Baja California State, which share a border with the USA.Measurements were carried out with an AccuScan RSD3000 remote sensing system for on-road vehicle emissions, to obtain concentrations of carbon monoxide, hydrocarbons and nitric oxide (CO, HC and NO) from exhaust fames.To determine the activity, composition and technological characteristics of vehicles, surveys were conducted and analyses of databases were made.The results show that the average of CO and HC from Mexicali and Tijuana are lower than in the Mexico City Metropolitan Area (MCMA), while the average for NO is higher.This study is the first effort conducted by Mexican environmental authorities to document the impact of imported uses cars in emissions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".