Tailpipe Emissions and Fuel Economy for 2WD Vehicles and AWD Vehicles Tested on a Double-Axle Chassis Dynamometer: A Comparative Study
Bibliographic record
Abstract
Tailpipe emissions, fuel consumption, and wheel torque data were measured for three pairs of vehicles tested over four drive cycles at the Emissions Research and Measurement Section of Environment and Climate Change Canada in Ottawa, Ontario. Each pair of vehicles included identical vehicle models; one vehicle was equipped with an AWD drivetrain and one vehicle was equipped with a FWD drivetrain. The AWD vehicle was tested on a double-axle chassis dynamometer. The amount of AWD activity was heavily dependent on driving behavior and AWD system design. During periods of torque delivery, the percentage of AWD activity ranged between 32% and 57% for the FTP-75 drive cycle, between 3% and 8% for the HWFCT drive cycle, and between 21% and 29% for the US06 drive cycle. The fourth drive cycle was the FTP-75 driven at -7°C. AWD distributions did not show sensitivity to temperature for the first and second vehicle models. The third vehicle model showed increases in AWD activity ranging from 27% to 33% at cold temperature. Vehicle fuel consumption increased with the amount of AWD activity, but was more sensitive to the additional weight of AWD components. Fuel consumption increases ranged between 2.2% and 13.9% for the FTP-75 drive cycle, between 1.6% and 7.8% for the HWFCT drive cycle, between 0.4% and 7.7% for the US06 drive cycle, and between 9.0% and 15.7% for the cold temperature FTP-75 drive cycle. There were no statistically significant differences in emissions of CO, NOx, and THC between the FWD and AWD vehicles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".