U.S. Department of Energy – Advanced Vehicle Testing Activity: Plug-in Hybrid Electric Vehicle Testing and Demonstration Activities
Bibliographic record
Abstract
The U.S. Department of Energy’s Advanced Vehicle Testing Activity tests plug-in hybrid electric vehicles (PHEV) in closed track, dynamometer, and on-road testing environments. The purpose of this testing is to determine the potential of PHEV technology to reduce petroleum consumption. It also allows documentation of PHEV driving and charging profiles and electric charging infrastructure requirements. As of March 2009, the Advanced Vehicle Testing Activity has initiated testing on 12 PHEV models from aftermarket conversion companies and original equipment manufacturers. In addition to performing controlled dynamometer and on-road testing, AVTA has collected in-use data from 155 PHEVs operating in 23 U.S. states and Canadian provinces. This fleet has demonstrated an average increase in cumulative fuel economy of 22 to 55% when in charge depleting mode, as compared to charge sustaining mode. Charge depleting range has varied from 32 to 64 miles, depending on the vehicle and battery pack. In ideal conditions, some vehicles have achieved monthly fuel economy results of 80 to 120 miles per gallon through frequent charging and less aggressive driving styles.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".