Bibliographic record
Abstract
This article surveys different state departments of transportation for winter maintenance technologies. Focus is on the software capabilities of the Federal Highway Administrations Maintenance Decision Support System (MDSS). The MDSS uses state-of-the-art weather forecasting, data fusion, and optimization techniques and integrates them with computerized winter road maintenance rules of practice. MDSS ultimately provides agencies with a specific forecast of road surface conditions and treatment recommendations customized for snow plow routes, such as whether to plow or use chemicals or abrasives, and when to apply treatments. The article includes descriptions of MDSS field demonstrations in Iowa, Michigan, and Canada. describes MDSS subsystem support from the National Weather Service and the National Oceanic and Atmosphere administration Forecast systems laboratory are detailed and MDSS system capabilities listed. Demonstrations of MDSS in the field in Iowa, Michigan and Canada are chronicled. A brief discussion of some the newest winter maintenance technologies is included such as pre-wetted salt, infrared thermometers, high speed spreaders, rubber snowplow blades, automatic vehicle location (AVL) systems using Global Positioning System (GPS), advanced road weather information systems (RWIS), and automatic bridge de-icing systems
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".