Lester B. Pearson International Airport Design and Construction of the Central Deicing Facility
Bibliographic record
Abstract
The Central Deicing Facility (CDF) at Lester B. Pearson International Airport (LBPIA) was an initiative first developed in 1996 under the direction of Transport Canada. Maintaining flight surfaces clean of ice and snow, simplifying communications between controllers, pilots and deicing crews; and minimizing water pollution, were all factors governing the design of this project. An airline accident at Dryden, Ontario on March 10, 1989 and a collision between a deicing vehicle and a Boeing 747-400 on January 21, 1995 at Dorval, Quebec were both examined closely to mitigate the potential of similar incidents happening. Centralization of the de-icing process was determined to be an essential first step in meeting these goals. The facility was located to minimize aircraft holdover time (eliminating the need for `re-sprays') whilst maximizing containment of effluent and increasing throughput to meet or exceed future launch capacity. The project was also initiated to replace existing de-icing positions at the perimeter of the Terminal 1 and 2 Aprons. The construction of the CDF project was ultimately constructed under the direction of the Greater Toronto Airports Authority between April 1998 and October 1999.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".