Bibliographic record
Abstract
The September 11, 2001 terrorist attacks on New York City and Washington D.C. had tremendous implications on airlines and airport security. Although the airline industry had been competing in a difficult climate before the attacks, the grounding of US and Canadian airlines and closing of North American airports in the aftermath cost airlines a combined estimated total of $1 billion per day. In passing the Air Transportation Safety and System Stabilization Act, the US government has tried to assist the ailing airlines with $5 billion in compensation for direct losses, $10 billion in Federal loan guarantees and credits, and limits individual air carrier liability for the events of September 11. The attacks have affected European airlines by contributing to the financial meltdown of Swissair, increased cutbacks in British Airways, Virgin Atlantic, Air France, Alitalia, Lufthansa, and others. The international economy's dependency on the stability of the airline industry is prompting governments in Europe and the United States to consider security possibilities to protect airlines and the industry in the future. While the US and Europe have faced significant cuts, the rest of the world's airline industry has seen relatively little impact. At airports, the attacks have caused temporary suspension of plans for development but construction is scheduled to continue.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.020 |
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".