Bibliographic record
Abstract
testing and evaluating prototype technologies designed to prevent or mitigate the severity of motor-vehicle collisions. The ultimate objective of these programs is to determine the safety benefits of these systems while fostering the development and commercial release of collision-avoidance systems for passenger cars and commercial heavy trucks. The U.S. DOT’s Integrated Vehicle-Based Safety Systems (IVBSS) initiative is now a part of these efforts. The University of Michigan Transportation Research Institute, with private partners, is currently carrying out this initiative in cooperation with the U.S. DOT. As part of the U.S. DOTs continued commitment to keeping industry informed of the progress and preliminary results of the IVBSS initiative, the accompanying document is being sent to leading industry organizations for further distribution among their members to review. Attached, in their preliminary and unpublished form, are the IVBSS Preliminary Performance Guidelines – Heavy Truck Platform. This document is in draft form and will be subject to revisions as the project progresses. The final version of this document will be made publicly available in the first quarter of 2008. While the document was created as part of a U.S. DOT-funded initiative it does not necessarily reflect the views of the National Highway Traffic Safety Administration (NHTSA) or the U.S. DOT, and should not be taken as such. We ask that you please distribute this document to your members for their information and review. Written questions or comments from your members regarding the Preliminary Performance Guidelines can be submitted directly to me at
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.335 | 0.263 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".