Development of the Des Moines Access Management Plan, 2004
Bibliographic record
Abstract
According to Iowa crash records, almost 10% of all crashes in Iowa occur at commercial driveways. Most of these crashes occur on arterials within municipalities. In recent years, nearly a quarter of these crashes have occurred in the Des Moines metropolitan area. This makes the Des Moines metropolitan area a prime candidate for improved access management. Case study research in Iowa has shown that access management is an extremely effective highway safety tool—well-managed routes are, on average, 40% safer than poorly managed routes. The Des Moines metropolitan area has many miles of four-lane, undivided arterials constructed when less was known about the importance of managing access to adjacent land development. This project involved a cooperative effort of the Des Moines Area Metropolitan Planning Organization (Des Moines Area MPO) and the Center for Transportation Research and Education (CTRE) at Iowa State University to develop a comprehensive access management study and program for the Des Moines metropolitan area. The goal of the study is to use the knowledge developed to make improvements that will reduce access-related crashes. It is also anticipated that this project will help local officials make better decisions about access management so that future safety and operational problems can be avoided.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".