AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for the National Park Service: Fort Vancouver National Historic Site
Bibliographic record
Abstract
Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory, is the lead laboratory for the U.S. Department of Energy’s Advanced Vehicle Testing. Battelle Energy Alliance, LLC contracted with Intertek Testing Services, North America (ITSNA) to collect data on federal fleet operations as part of the Advanced Vehicle Testing Activity’s Federal Fleet Vehicle Data Logging and Characterization study. The Advanced Vehicle Testing Activity study seeks to collect data to validate the use of advanced electric drive vehicle transportation. This report focuses on the Fort Vancouver National Historic Site (FVNHS) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of electric vehicles (EVs) into the agencies’ fleet. Individual observations of the selected vehicles provided the basis for recommendations related to EV adoption and whether a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles) could fulfill the mission requirements. FVNHS identified three vehicles in its fleet for consideration. While the FVNHS vehicles conduct many different missions, only two (i.e., support and pool missions) were selected by agency management to be part of this fleet evaluation. The logged vehicles included a pickup truck and a minivan. This report will show that BEVs and PHEVs are capable of performing the required missions and providing an alternative vehicle for both mission categories, because each has sufficient range for individual trips and time available each day for charging to accommodate multiple trips per day. These charging events could occur at the vehicle’s home base, high-use work areas, or in intermediate areas along routes that the vehicles frequently travel. Replacement of vehicles in the current fleet would result in significant reductions in emission of greenhouse gases and petroleum use, while also reducing fuel costs. The Vancouver, Washington area and neighboring Portland, Oregon are leaders in adoption of PEVs in the United States1. PEV charging stations, or more appropriately identified as electric vehicle supply equipment, located on the FVNHS facility would be a benefit for both FVNHS fleets and general public use. Fleet drivers and park visitors operating privately owned plug-in electric vehicles benefit by using the charging infrastructure. ITSNA recommends location analysis of the FVNHS site to identify the optimal station placement for electric vehicle supply equipment. ITSNA recognizes the support of Idaho National Laboratory and ICF International for their efforts to initiate communication with the National Parks Service and FVNHS for participation in this study. ITSNA is pleased to provide this report and is encouraged by the high interest and support from the National Park Service and FVNHS personnel
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".