Examination of the Traffic Safety Environment During the Second Quarter Of 2020 Special Report
Bibliographic record
Abstract
The National Highway Traffic Safety Administration is reviewing national changes in roadway travel and changes in drivers’ behavior that have occurred since the start of the COVID-19 public health emergency, with an emphasis on the second quarter (Q2) of 2020. Most important, we are learning about the impact on motor ve-hicle crashes and fatalities. This report draws from an array of sources to bring together as much information as possible to provide an understanding of our current traffic safety environment, and to better address our chang-ing traffic safety needs. Prior economic downturns, such as the financial crisis of 2008, provide some compari-son for reduced roadway travel and changes in travel patterns. As this report documents, although there are some similarities with that time frame, there are many differences in impact on speeding and other dangerous driving behaviors, such as reduced seat belt use. This report explores changes in countermeasure use including traffic enforcement and public communications and outreach. This report also examines the question of whether some people – who continued driving even when many communities had stay-at-home guidelines – may be inherently higher-risk drivers. The report draws on sources such as emergency medical services (EMS) and hospital trauma center data as we examine this issue.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".