Comparison of mobile source emission models using aggregated and disaggregated data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it