Bibliographic record
Abstract
While plowing and salting to de-ice are still important in winter road maintenance, the reported use of anti-icing strategies is growing quickly. According to a survey sponsored by members of the Strategic Highway Research Program's Anti-Icing/RWIS Lead States Team, 86% of states responding to the survey planned to expand or start anti-icing. Between 1997 and 1999, the proportion of DOT vehicles equipped for anti-icing rose from one-in-10 to one-in-five. The number of lane miles treated jumped 50% in the same period. About 75% of the states responding to the survey used Road Weather Information Systems to help determine the need for anti-icing. All of the states were using embedded pavement sensors. Anti-icing was less popular in Canada, the survey indicated. Half of Canada's 10 provinces reported using anti- icing on a total of 2.3% of their lane miles. Meanwhile, researchers at Michigan Technological University's Institute of Snow Research are developing a new technology called Anti-Icing Smart Overlays. By gluing ground rock to the pavement with epoxy, engineers hope to create an overlay that soaks up de-icing chemicals so they won't need to be reapplied every time it snows. A likely first use is on highway bridges.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".