Fuel Consumption Track Tests for Tractor-Trailer Fuel Saving Technologies
Bibliographic record
Abstract
The objective of the project was to conduct controlled test-track studies of solutions for achieving higher fuel efficiency and lower greenhouse gas emissions in the trucking industry. Using vehicles from five Canadian fleets, technologies from 12 suppliers were chosen for testing, including aerodynamic devices and low rolling resistance tires. The participating fleets also decided to conduct tests for evaluating the impact on fuel consumption of vehicle speed, close-following between vehicles, and lifting trailer axles on unloaded B-trains. Other tests targeted comparisons between trans-container road-trains and van semi-trailers road-trains, between curtain-sided semi-trailers, trans-containers and van semi-trailers, and between tractors pulling logging semi-trailers loaded with tree-length wood and short wood. The impact of a heavy-duty bumper on fuel consumption and the influence of B5 biodiesel blend on fuel consumption were also assessed. The test procedure followed the SAE J1321 Joint TMC/SAE Fuel Consumption Test Procedure - Type II. With payback periods as short as 0 to 9 months, some technologies and approaches represent viable measures for reducing fuel consumption. Conservative estimations show that these technologies could bring annual Greenhouse Gases Emissions reductions of 6.7 tonnes per vehicle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".