Automating The Deicing Process: A Case Study at Toronto Pearson International Airport
Bibliographic record
Abstract
The Greater Toronto Airports Authority (GTAA) is responsible for the day-to-day operation at Pearson International Airport, the busiest airport in Canada. During winter, the process of deicing/anti-icing of aircraft is key to the safe, efficient flow of air traffic through this hub airport. GTAA has a central deicing facility (CDF) comprised of an icehouse cab, staging/deicing pad areas and associated taxiways, and glycol blending and recycling facilities for deicing operations at Pearson. Aircraft are guided though the deicing process at the CDF by 48 signboards placed in the middle and at either end of pad safe zones. The signboards are connected through fiber links to the CDF's Bay Management System, which automates the movement and positioning of aircraft on the staging/deicing bays through visual instruction to pilots. In conjunction with verbal instruction, the Bay Management System provides a safe and efficient means of guiding aircraft through all phases of deicing/anti-icing operations.
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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) | 0.000 | 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".