Safety Evaluation of Centerline Rumble Strips. Final Project Report
Bibliographic record
Abstract
4,932 words, 6 figures + 4 tables @ 250 words each 7,432 equivalent words TRB 2004 Annual Meeting CD-ROM Paper revised from original submittal. The objective of this research was to evaluate the effectiveness of centerline rumble strips in reducing cross-over-the-centerline crashes and improving the safety of undivided roadways. Twenty U.S. states, along with several Canadian provinces, are currently using centerline rumble strips. A detailed analysis of crashes on State Routes 2, 20, and 88 in Massachusetts, before and after the installation of centerline rumble strips, showed no significant change in crash frequencies after the installation of centerline rumble strips; however, no fatal crashes have occurred on State Routes 2 and 88 since the installation of centerline rumble strips. Three cross-over-the-centerline fatal crashes did occur on State Route 20 after the centerline rumble strips were installed; centerline rumble strips where not a countermeasure to these specific crash types. Driver behavior at shoulder and centerline rumble strips was evaluated using a full-scale driving simulator. Drivers were found to react and correct the vehicle trajectory more quickly with
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".