Incidence of a new proposal syllabus for teaching aeronautical phraseology in english at Cap. Fernando Vasconez Army Aviation School for XXIX basic pilots course 2006
Bibliographic record
Abstract
Differences in the use of ICAO (International Civil Aviation Organization) phraseology in some countries complicate understanding; especially to pilots whose English is an acquired language. United States and Canada form a region where such differences are mostly noticeable. \nEffective communication starts with the knowledge and respect of ATC procedures and aircraft performance data. Effective radio communication abroad also requires proficiency in general English. A well-known saying ¿Seeing once is better than hearing twice¿ gives the worst fit when it concerns radiotelephony phraseology. While preparing for flights in the North America and looking through materials that were at hand, there was no expectation of a bolt from the blue. But the first landing at Gander, followed by Ottawa, Toronto, Boston, New York, Chicago, Andrews and McGuire Air Force Bases, have made it clear that what was read by eyes in a book was rather difficult for ears to hear. A deeper study of American aviation regulations has shown that radio communication over the US was a trouble not only to the crews, but also to American regulating authorities and aviation industry. In the fall of 1988 the FAA (Federal Aviation Administration) and airlines called for a joint action to improve communications between pilots and controllers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.159 | 0.051 |
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".