CONTROLLING WITH SASS : COMPUTING POWER TAKES THE 'GRUNT' WORK OUT OF ASSIGNING LANDING SLOTS AT BUSY AIRPORTS
Bibliographic record
Abstract
Airport acceptance rates (AAR) as they change can contribute to aircraft arrival and departure bottlenecks. Through the use of metering and scheduling tools that calculate and update arrival times, delays can be reduced and arrival flows optimized. This article describes a Scheduling and Sequencing System (SASS) that is scheduled for testing at the Lester B. Pearson International Airport in Toronto, followed by further testing at the Vancouver and Calgary International Airports. The manual tasks leading to and including calculated landing slots for aircraft will be taken over by the SASS which can rewrite arrival flow schedules, tight en up separation, and equitably distribute delays among all aircraft. This article describes the various features of the SASS and gives a detailed overview of how calculations are derived. Results from a simulation study examining the potential benefits of a new metering tool showed that an aircraft could save up to 5.8 minutes, depending upon the AAR, in comparison to the manual metering systems currently used by controllers.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".