ANALYSIS OF ROAD WEATHER INFORMATION SYSTEM USERS IN CALIFORNIA AND MONTANA
Bibliographic record
Abstract
A road weather information system (RWIS)--a network of weather stations, forecasting services, and the supporting infrastructure--has been used widely in the United States and Canada since the late 1980s. Through separate projects with Montana Department of Transportation (DOT) and California Department of Transportation (Caltrans), Western Transportation Institute (WTI) collected information from road weather information users through surveys and interviews. Montana DOT's survey, completed in September 2000, received responses from 89 Montana DOT maintenance personnel. WTI conducted the Caltrans study in January 2002, and received responses from maintenance and traffic operations staff representing 11 of the 12 districts. Although not identical, the surveys included questions in similar categories, including training; current use, methods, and data; station siting; and accuracy. This paper summarizes the RWIS operations and user opinions in California and Montana and compares them with those reported by Wyoming DOT in 1998. Specifically, this analysis discusses: RWIS user profiles; station siting and networking; weather information improvement ideas; perceived current and potential usefulness; training; and traffic operations and maintenance usage. The objective of this analysis is to identify nationally applicable RWIS trends or improvements. The information in this paper will be of interest and benefit to transportation officials who wish to gain a better understanding of users' perspectives on RWIS, identify areas of improvement for a state's RWIS, and learn about related experiences from other states.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".