Bibliographic record
Abstract
Track testing has shown significant fuel-savings promise for truck platooning strategies but also raised unexpected questions about close following and long-distance following scenarios that could significantly impact savings realized in real-world conditions. The 2017 track test collaboration among NREL, Lawrence Berkeley National Laboratory, NRC Canada, Transport Canada, and others included onboard instrumentation to help the team gain a deeper understanding of the dynamic interaction between multiple vehicles. NREL's approach includes detailed data analysis for additional onboard sensors and J1939 CAN bus data from 2017 track test to investigate following truck air flow and turbulence changes to explain reduced savings at close following distances for the last vehicle in a platoon, define engine-cooling impacts of platooning position in different formations due to reduction of ram air through front grill and generate an understanding of a true in-use "baseline" with other vehicles on the highway. Initial data analysis indicates many of the data trends in wind angle, wind speed, and temperatures show a change in pattern for the closer following distances where fuel savings decrease for the following vehicles was also documented. This is encouraging in that the planned further analysis may yield the desired insights into the cause of the reduced savings. Once analyzed, light-duty vehicle and dynamic scenarios will help us refine the sensitivity of findings from the standard platooning scenarios.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".