ANTI-ICING ON STRUCTURES USING FIXED AUTOMATED SPRAY TECHNOLOGY (FAST) : DEMONSTRATION PROJECT, PRESCOTT, ONTARIO
Bibliographic record
Abstract
This paper overviews the selection, design, implementation and performance of Canada's first fixed automated anti-icing spray system installation for a highway/roadway application. Installed in the fall of 2000 on the northbound 416/401-interchange structure, the systems has been in service for the entire winter of 2000/2001. The construction of this new bridge was completed in September of 1999. During the first winter of operation a number of weather related accidents occurred on the structure. For a number of years the Ministry of Transportation, Ontario (MTO) has been investigating anti-icing and (Advanced) Road Weather Information Systems ((A)RWIS) as independent approaches and systems to complement the established levels of service for roads during winter storms. Based on its own research and the experience of othe agencies, MTO believed that there was an opportunity to significantly reduce the potential for icing on the structure. This could be achieved by remotely sensing potential frost and ice and automatically applying a liquid deicing chemical before it actually formed. The FAST system continuously monitors conditions on the structure and based on the detection of critical threshold parameters it automatically sprays the chemical just in advance of icing conditions. The structure in question is a 165m super-elevated, high speed, freeway-to-freeway ramp with a design speed of 130km/hr and a 3000 AADT. Since putting the system into service there have been no weather related accidents. The Ministry and its maintenance contractor have also taken this opportunity to evaluate the performance of Liquid Potassium Acetate, a chemical which is not on Environment Canada's list of road deicing chemicals which are under consideration to be designated as toxic under Section 64 of the Canadian Environmental Protection Act, 1999 (CEPA 1999). This report reviews the FAST installation at the site, the roles of the partners in the implementation and operation; how a desire for enhanced response time, increased safety, and reduced environmental impact, resulted in study and implementation within a 6 month time period; project costs; approach to study, design, procurement, contracting and risk sharing; lessons learned during design and through the operation to date and other relevant points which may be of interest to road authorities. For the convering abstract of this conference see ITRD number E201066. (A)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".