Groundside Interview Survey Concepts and Design: Toronto Pearson International Airport, June 2005
Bibliographic record
Abstract
This paper covers the requirements and design phases of the 2005 Groundside Survey at Toronto Pearson International Airport conducted in June 2005. The design process will be covered under the following general topics: data requirements, data use, value of the date and survey concepts. This last topic will be further developed in terms of the core interview surveys, the supporting data, aspects of the data coding and entry phase and the final file linking process. It is these interview surveys and the handling of the data that will create the statistical database that will be used for the generation of air passenger characteristics and distribute these passengers, in time and space, across the groundside facilities with respect to their corresponding flight and its characteristics. The ability to statistically distribute individual flight passenger loads to groundside facilities, over time, is paramount to the development of groundside operational and planning models. Furthermore the survey method allowed for the defining of the catchment area of the airport, both the orientation of trips to the Toronto area and the wider dispersion of trips within Southern Ontario and beyond. This survey methodology allows for the both the routine analysis for modal split with respect to air trip purpose as well as the development of air passenger lead-lag curves for future planning of groundside facilities for a given airside schedule. Conclusions in the paper will focus on the lessons learned in the design, as well as examples of key statistical findings.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".