SELF-TAG BAGS : PASSENGERS ARE AVOIDING CHECK-IN COUNTER QUEUES BY TAGGING THEIR OWN BAGGAGE IN A MONTREAL PILOT PROGRAMME
Bibliographic record
Abstract
Self check-in by passengers at automated kiosks in airports could be joined by self-tagging of checked luggage, to judge by results from a pilot project in summer of 2004 at Canada's Montreal-Trudeau International Airport. It allows trans-border check-in and checking of luggage by passengers. More than 6,600 transactions were recorded in the first four months of the pilot project. It was a collaboration among the United States Transportation Security Administration (TSA), SITA, Transportation Canada and U.S. Customs. The authority for the airport initiated the program and supported it with funding to develop software to enable bag tag printing, known as AeroCheck. There were three goals: eliminate lines, reduce the amount of staff airlines have to use in the process and free up terminal space. At US Airways, some 20% of passengers were tagging their own bags. To participate, people had to pass through a three-question screening process: did they pack their bags themselves, did they leave them unattended at any point since leaving home, and were they aware of what their bags contained. After receiving boarding passes and baggage tags, they proceeded to a pre-clearance area. The automated system is projected to handle about double the flow of passengers compared to traditional check-in counters. As a result of the original test success, the authority is installing a one-stop self-tagging area in a new courtyard, with passengers then depositing their bags in a common area where the tags are scanned and bags directed to proper carrier.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".