Comparison of mobile source emission models using aggregated and disaggregated data
Bibliographic record
Abstract
Mobile source emission models are designed to provide a quantification tool of the amount of pollution that is released to the atmosphere from the vehicles within a defined region.The most common models were developed based on aggregated data, such as vehicle miles travelled, fuel consumption and average travelling speed.Recently, new models have been developed.They use a disaggregated analysis approach in order to include the sudden changes in speed and acceleration and the traffic interactions in the calculation.This paper presents a comparison between three different models developed on three different levels of data aggregation and their application on a road stretch.Traffic data (speed, acceleration and flow) is extracted from a micro simulation model and then used to calculate the total emissions during a specific period of time.Emission data was collected using a Portable Vehicular Emission Measuring System in a chassis dynamometer on a second by second basis.The main purpose of this research is to show the main differences in the calculation of emissions and their applicability to different levels of emission inventories.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".