Bibliographic record
Abstract
W ith about one-third of all aviation accidents associated with runway operations, the International Civil Aviation Organization (ICAO) has introduced safety initiatives aimed at reducing runway-related accidents. The initiatives were endorsed in late May by ICAO partners within the aviation industry, including Flight Safety Foundation, during the first meeting of the ICAO Global Runway Safety Symposium, held in Montreal.1 “We have a clear understanding on the roles and responsibilities of each of the partners in reducing and working toward eliminating runway incursions and excursions,” Nancy Graham, director of the ICAO Air Navigation Bureau, said. “The multidisciplinary approach is the only option for coming to grips with a complex set of operational and human factors issues.” The initiatives include runway safety seminars to be held around the world to help develop regional action plans and encourage the formation of runway safety teams that will involve airlines, airports and air navigation service providers. Other efforts call for “the compilation and further development of best practices and the greater sharing of information among ICAO member states and industry.” One of the first requirements will be the development of common definitions, metrics and methods of analysis to enable more complete information sharing, as well as the improved reporting of operational hazards. © K en C ol e/ D re am st im e
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".