Embracing Common Use: Toronto Pearson Installed 96 Common-Use Self-Serve Kiosks in 2007
Bibliographic record
Abstract
This article describes some steps toward “common use” facilities at airports, as shown at the Toronto Pearson International Airport, operated by the Greater Toronto Airports Authority (GTAA). The GTAA has installed nearly 100 common sue self-serve kiosks in two of its terminal, accounting for one quarter of the reported 406 such kiosks in nine of Canada’s biggest airports. They serve domestic, transborder and international passengers. Any airline operating out of Pearson my use the kiosks, though they still aren’t fully integrated with airline back-office systems. Of the 75-plus airlines operating at Pearson, fewer than 10 are in the kiosks. Passengers seem more amenable to them than the airlines. Airlines are able to customize some kiosks close to the airlines’ gates, and the kiosks’ contribution to cutting labor costs for processing tickets should encourage greater adoption. Self-serve baggage tagging is also being tested at common use kiosks. At one point during the test, some 20 percent of one airline’s passengers were using the system.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".