Bibliographic record
Abstract
Commercial medium and heavy vehicles, though only a small portion of total vehicle population, play a significant role in energy consumption. In 2012, these vehicles accounted for about 5775.5 trillion btu of energy consumption and 408.8 million tons of CO2 emissions annually, which is a quarter of the total energy burden of highway transportation in the United States [1]. This number is expected to surpass passenger car fuel use within the next few decades. In the meantime, most commercial vehicle fleets are running at a very low profit margin. It is a well known fact that fuel economy can vary significantly between drivers, even when they operate the same vehicle on the same route. According to the US Environmental Protection Agency (EPA) and Natural Resource Canada (NRCan), there is up to 35% fuel economy difference between drivers within the same commercial fleet [2] [3], [4]. Similar results were obtained from a Field Operation Test conducted by Eaton Corporation [5]. During this test as much as 30% fuel economy difference was observed among pick-up-and-delivery drivers and 11% difference was observed among line-haul drivers. The driver variability can be attributed to the fact that different drivers react differently to driving conditions such as road grade, traffic, speed limits, etc. For instance, analysis of over 600k miles of naturalistic heavy duty truck driving data [5] indicates that an experienced driver anticipates a downhill and eases up on the throttle to save fuel while an inexperienced driver lacks this judgment.
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.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".