What can be learned from The EV Project to inform others who may be interested in a similar study?
Bibliographic record
Abstract
In 2009, a project proposed by Electric Transportation Engineering Corporation (ETEC, a subsidiary of ECOtality Inc.) was selected via a competitive bid process as part of the American Recovery and Reinvestment Act’s (ARRA) Funding Opportunity Announcement 28 - Transportation Electrification. At the start of the fourth quarter of 2009, ETEC signed an agreement with the US Department of Energy (DOE) as recipient of a cost-share grant funded by the ARRA to study the use of charging infrastructure by Plug-in Electric Vehicle (PEV) drivers. The project was subsequently called The EV Project. By studying the use of the vehicles and the charge infrastructure, the EV Project would attempt to understand; • how many publicly accessible charge sites would be needed to promote widespread adoption of EVs, • where and at which venues should publicly accessible charging sites be located to be most effective, • which type of charging (AC Level 2 vs DC Level 2 (fast chargers)) would be most utilized by PEV driver’s, • how much would EV drivers be willing to pay for the convenience of away-from-home charging, • what impact did (and would) the PEV charging at (residential and publicly accessible) sites have on the local electrical grid, • which type of away-from-home charging was of more interest, workplace or publicly available, • could charging site hosts realize enough benefit from the chargers to support the deployment levels desired or needed, and • how did the geographically diverse regions of the United States differ with respect to the above? This paper examines The EV Project to learn what was done to meet the ambitious objectives of this infrastructure study, and what was learned from both failures and successes. What can the experience of having conducted The EV Project inform others about what worked, what didn’t work, and what could be done better if undertaking a similar project?
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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 | 0.117 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".