Crash Modification Factor Recommendation List
Bibliographic record
Abstract
Practitioners use Crash Modification Factors/Functions (CMFs) to calculate the number of crashes expected once a countermeasure has been implemented. When evaluating design alternatives, CMFs can be used in conjunction with safety performance functions (SPFs) to derive crash predictions. The Federal Highway Administration (FHWA) maintains the CMF Clearinghouse as a repository for CMFs. Contributions are submitted by researchers across the U.S., Canada, and throughout the world. Typically, multiple CMFs are associated with a single countermeasure. Some CMFs only apply to specific facility or crash types. Furthermore, CMF quality varies, and some only apply to specific facility and/or crash types, regions, or times. Using the Clearinghouse to identify an appropriate CMF for a given situation demands considerable time and significant expertise. This report describes the development and implementation of a spreadsheet-based tool that Kentucky Transportation Cabinet staff and the agency’s design consultants can use to select CMFs most appropriate for the state’s highways and conditions. Guidance instructs users on use of the tool. A web-based form was also developed, which must be filled out and submitted to KYTC’s CMF committee when a CMF is planned for use on a project.
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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.447 | 0.281 |
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".