Bibliographic record
Abstract
Airplane fire protection demands a very high level of reliability. In flight there is no escape from a fire and with an abundance of fuel and ignition sources, the threat of a fire onboard an airplane is ever present. Today's airplanes comply with existing fire protection regulations. The regulations affecting fire protection change with the advent of new technologies and experiences. This paper addresses methods for fire protection in the design of new airplanes. Prevention of a fire is the best method of fire protection, for it is best to prevent a fire than to have to deal with a fire in flight, but dealing with a fire in flight may become inevitable at one point or another. This is why fire protection methods such as passive methods and active methods are addressed. This paper addresses various fire protection methods from eliminating fuels and ignition sources to reducing flammability, from zoning and compartmentation to material selection and ventilation, from temperature control to fire detection and fire extinguishing or fire suppression systems. In addition the fire protection design basis for all areas of the airplane, from radome to the tail that include the flight deck, engines, auxiliary power unit (APU), cabin, cargo compartments, fuel tanks, lavatories, crew compartments, electrical and electronics compartment, accessory compartments and the tail compartment are discussed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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".