PHX Sky Train: APM Adaptation for Phoenix
Bibliographic record
Abstract
As the American Southwest is one of the fastest growing areas on the continent and with airline passenger demands expected to continue to increase, the Phoenix Sky Harbor International Airport must continue to adapt and improve to meet the demands being placed on its facilities, roadway systems and terminal areas. To that extent, the City of Phoenix has embarked upon a modernization and expansion program at Sky Harbor International Airport that accommodates passenger demands and traffic congestion. As part of this modernization and expansion plan, a new automated train system is being provided to ease traffic congestion on and around the Airport access roads and to serve as a critical link between existing and future infrastructure. To realize this critical link, Bombardier Transportation is providing the state-of-the-art 2.2 mile (3.5Km) pinched-loop BOMBARDIER INNOVIA APM 200 automated people mover system including 18 vehicles outfitted with the BOMBARDIER CITYFLO 650 signaling system. The intent of this paper is to describe the scope of supply including the vehicles, Automated Train Control system, guideway components, and maintenance facility. This paper will also discuss how Bombardier through the design, manufacturing, and testing phases, will solve several technical challenges. These challenges include modification of key system elements to deal with the harsh desert environment, establishing provisions for expandability to enable timely cutover without impacting system operation, and streamlining of manufacturing, and testing and commissioning activities to adapt the overall system to the Phoenix Sky Harbor application and meet the tight project schedule.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.019 |
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".