Crowd behaviour in Canadian football stadia — Part 1: Data collection
Bibliographic record
Abstract
Large crowd sizes at stadia events require an in-depth consideration of human behaviour, since the reliability of egress models in design depends on the confidence of the input data. However, there is little contemporary public data surrounding crowd behaviour, and its implementation into pedestrian movement models, particularly focused on Canadian demographics in stadia. A novel data collection (Part 1) and subsequent egress validation modelling of a Canadian stadium were completed to examine the variability of simulations with behavioural inputs (Part 2). The demographic distribution, pedestrian speed, exit and route choice, and areas of congestion were quantified using high resolution cameras. Behaviourally, pedestrians exited the stadium where they entered which created high levels of cross flow. It was observed that contemporary walking speed profiles for stadia will differentiate from classical profiles especially with reflection of demographic distribution by as much as 31%. Individual walking speeds, while highly variable, impacted overall egress time. Crowd density also being a factor that further reduced their speed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".