Bibliographic record
Abstract
Applying anti-icing chemical at the optimum time is critical for an effective anti-icing program. The timing of anti-icing chemical applications is especially critical for bridge structures, where icing can occur in advance of icing on normal pavements. Additionally, other highway characteristics such as ramps and steep grades can require different treatment strategies as compared to anti-icing treatments for normal pavements. Advances in technology have given highway operators a new tool to enhance the effectiveness and efficiency of their anti-icing program. This tool is the fixed automated spray technology (FAST) system. The FAST system is a permanent installation of a pump, a tank, nozzles, and a controller that dispenses anti-icing chemicals directly on a predetermined area of pavement. These systems can initiate chemical applications either on manual command or be integrated with a road weather information system (RWIS) to operate automatically based on detected highway conditions. The FAST system permits timely, localized, and repeated anti-icing treatments with the optimum amount of anti-icing chemicals and without the deployment of typical winter maintenance equipment and personnel. FAST systems are now in use in more than 20 U.S. states and in several locations in Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".