An Electric-Drive Vehicle Strategy for Sweden
Bibliographic record
Abstract
Author(s): Sperling, Daniel; Lipman, Timothy E.; Lundberg, M. | Abstract: EVS-17, Montreal, Canada, October 15 - 18, 2000The large environmental impacts caused by Sweden's transport sector can be mitigated by exploiting a variety of technological innovations, especially electric-drive technologies. This paper explores an electric-drive vehicle strategy for Sweden. The strategy takes into account attributes of Sweden and the state of knowledge and experience with electric-drive technology.Sweden's unique attributes include inexpensive and clean electricity, a strong environmental ethic, and a strong automotive sector (with strong domestic industrial commitments to buses and trucks). The state of knowledge and experience with electric-drive vehicles is characterized as follows: virtually all versions of electric-drive technology are seen to be environmentally superior to internal combustion engine vehicles; some are potentially superior in terms of consumer desires; costs of batteries will drop but remain expensive; major automotive companies have mostly abandoned plans to build and market conventional-sized battery-electric vehicles, but are on the verge of deciding whether to make major investments in fuel cell electric vehicles; many automakers are beginning to make major investments in hybrid electric vehicles; and electric-drive buses are gaining increasing attention as a strategy to reduce emissions in urban areas.Given these observations, we explore the following strategy for Sweden:* industrial policy of designing and manufacturing heavy duty vehicles (buses and trucks) powered by electric drive;* environmental policy of deploying small electric vehicles for on and off-road transportation applications, as well as heavy duty electric-drive vehicles targeted by industrial policy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".