Bibliographic record
Abstract
Data about the mortality risk of scheduled passenger air travel over 2000–2007 around the world is examined in this paper. Worldwide, the average passenger death risk per scheduled flight over 2000–2007 was about one in 3.0 million. However, much as the center of mass of a doughnut is the center of the hole—where there is no mass—the worldwide average represents the actual risk level in few if any countries. The data support a three-population risk model across nations, in which the differences in death risk are not statistically significant within groups but are highly significant across groups. The safest nations are the traditional first-world countries (e.g., Canada, Japan), with a death risk per flight of about 1 in 14 million. Next safest are those developing-world nations that have either have recently attained first-world status (e.g., Singapore, South Korea) or are classified by experts as newly industrialized (e.g., Brazil, China) Their aggregrate death risk per flight was about 1 in 2 million. The least safe nations statistically are remaining developing-world countries, with a death risk per flight of about 1 in 800,000. In terms of relative risk, divergences within the developing world are modest compared to the overall difference between the first and developing worlds. The observed risk pattern might reflect a confluence of economic and cultural factors.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".