Bibliographic record
Abstract
The author explores the emergency management program at Toronto Pearson International Airport. Under the leadership of the Greater Toronto Airport Authority (GTAA), emergency management is part of GTAA’s Airport Development Program. Airport participation in emergency management improvement adopts the aims of the Canadian Standard Association in consolidating best practice codes with private and public industry. The impacts of world events having significant life safety, environmental, and cross-jurisdictional components (i.e., the avian flu pandemic, major air crashes) have been incorporated into the airport’s emergency management planning. One example the author cites is based on the increased needs and expectation of passengers for customer service, safety and security. Planners now routinely include all victims of air crashes (including the affected families), especially in the aftermath of high profile air crashes and resulting legislation enacted in the United States that obligates both the government and air carriers flying in U.S. airspace. Also discussed is the role of Toronto-based non-traditional airport support services (unique to the airport) that work as special teams complementing more traditional emergency response services. The teams profiled include the GTAA GO Team, Pearson Crisis Support Team, and Pearson Family Support Team. The annual exercise program is another aspect of emergency response that is unique to the Pearson Airport Emergency Response Program. It includes a schedule of one full scale, 10 partial deployments, and six tabletop exercises. Considered to be aggressive, the exercise program is an annual requirement mandated by the Board of Directors that involves commitment throughout the year from the airport, along with the emergency response and emergency management agencies of the surrounding communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".