Clarus Multistate Regional Demonstrations: Summary of Evaluation Results and Lessons Learned
Bibliographic record
Abstract
Clarus is a national data management system that integrates road weather data from multiple agencies and shares quality-checked surface transportation weather and pavement observations. Operated as an experimental system for demonstration and evaluation purposes, Clarus has grown substantially since its inception in 2004. As of August 2011, Clarus ingests data from over 2,250 environmental sensor stations in 38 states, four Canadian provinces, and five local department of transportation (DOT) organizations. To spur the growth of applications using Clarus data, the FHWA funded a phased multistate research effort to create and demonstrate operational decision tools in a real-world setting. In addition to an overall application demonstrating the enhanced weather forecasting capabilities possible using Clarus data, four new applications, termed use cases, were developed as part of this effort. These included tools for seasonal load restrictions decision support, nonwinter maintenance and operations decision support, multistate control strategy coordination, and enhanced road weather content for traveler advisories. Proof-of-concept prototype applications were developed by two different systems integrators and deployed by the participating state DOTs during 2010–2011. These deployments were independently evaluated for their potential to improve system operations. This paper summarizes the results of the independent evaluations, providing a glimpse into the value of these applications to state DOTs. Results show that the Clarus system enables innovative applications that provide a wide array of decision support system possibilities. While each of the applications has additional research requirements and implementation challenges to overcome, they demonstrate the potential to proactively change maintenance and weather-responsive management.
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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 | 0.035 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".