Examining the potential for voluntary fuel economy standards in the United States and Canada.
Bibliographic record
Abstract
This report is designed to assist the U.S. Department of Energy, the U.S. government in general, and Natural Resources Canada with understanding the potential for voluntary fuel economy standards designed to increase the fuel economy of the North American fleet of light-duty vehicles (LDVs-passenger cars and light trucks) within a 10-15-year timeframe. The approach of this study has been: First, to examine and evaluate recent fuel economy initiatives taken in Japan and Europe; Second, to review the technologies available to improve fuel economy in the U.S. (and Canadian) fleets, focusing on their costs and fuel economy improvement potential; Third, to identify and broadly evaluate some alternatives to the current U.S. and Canadian Corporate Average Fuel Economy system of specifying uniform fuel economy targets (27.5 mpg for cars, 20.7 mpg for light trucks) for individual companies; and Fourth, to try to determine an approximate level of fuel economy increase and form of company agreements that would be conducive to a voluntary agreement, based on the assumption that an acceptable voluntary standard would impose an equitable burden on each manufacturer and would be approximately cost-effective from consumers' private perspectives.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".