Trends and insight in heavy-duty vehicle electrification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes the results from Annex XII “Heavy-duty hybrid vehicles” of the International Energy Agency (IEA) Implementing Agreement on “Hybrid and Electric Vehicles” (IA-HEV). Since 1993, this Implementing Agreement has provided a platform to exchange knowledge, experience and strategies among the member states on the latest developments in hybrid and electric vehicles, mostly passenger cars. Because heavy-duty vehicles have specific technical requirements and economic boundary conditions compared to the passenger car market, a dedicated Annex was started in 2007 and has run for several years until the end of 2010. Six countries (Belgium, Canada, Finland, Switzerland, the Netherlands and the United States) have been participating to collect and share relevant information on the latest technical and market developments in “electrified” trucks, buses and mobile work machines. This information will broaden the insight in the existing applications of heavy-duty vehicles electrification and can provide essential information for future heavy-duty hybrid vehicle deployment projects.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it