Bibliographic record
Abstract
YOU MIGHT THINK 2014 WAS A BAD YEAR FOR FLYING. There were four highly publicized accidents–the still-mysterious disappearance of Malaysia Airlines Flight 370 in March, the shooting down of Malaysia Airlines Flight 17 over Ukraine in July, AirAsia Flight QZ8501 falling into the Java Sea in December, and finally, in July, the Air Algerie Flight 5017 crash in Mali, for a total of 815 dead. · But according to Ascend, the consulting branch of Flightglobal that monitors aircraft accidents, 2014 in fact had the best accident rate in history: one fatality per 2.38 million flights, compared to the previous best of one per 2.37 million in 2012. True, Ascend did not count the downing of MH Flight 17, which was an act of war, not an accident. · In any case, it's better to personalize the problem by putting it in terms of the risk per passenger per hour of flight. The necessary data are in the annual safety report by the International Civil Aviation Organization, which covers large jetliners as well as smaller commuter planes. · In 2013, 32.1 million domestic and international flights carried 3.1 billion people, logged 5.8 trillion passenger-kilometers, and experienced 90 accidents, causing 173 fatalities. With the mean flight time at about 2.2 hours, this implies roughly 6.8 billion of passenger-hours or 2.5 × 10-8fatalities per person per hour in the air. For large jetliners—dominated by Airbuses and Boeings and regional jets made by Canada's Bombardier and Brazil's Embraer–the risk that year was just 1 × 10-8. In 2014, large jetliner accidents (excluding the MH17) would have pushed the latter rate to about 8 × 10-8, but the mean for the past decade remains at historic lows.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.259 | 0.196 |
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".