Bibliographic record
Abstract
During its flight an aircraft can be struck by lightning and the induced high current will require a highly conductive airframe skin structure in order for it to propagate through with minimum damage. However an aircraft skin is generally coated with paint and the airframer does not always have control on the paint thickness. Paint thickness generates heightened concerns for lightning strike on aircraft, mainly because most of coatings dedicated to that purpose are non-conductive. Using insulating material or non-conductive coating with certain thickness may contribute to or increase damage inflicted by the swept stroke lightning energy, even on metallic structures Due to its high relative permittivity, a non-conductive paint or coating on a fuselage skin surface will contribute to slow down the lightning current propagation through structure. With this comes the risk of increasing heat that will favor structural damage and possible melt through. The correlation between paint thickness, lightning dwell time, and aircraft skin thickness are all contributing factors in lightning swept stroke damage in aircraft lightning Zone 2A. Paint thickness is not always under direct control of the manufacturer and the maximum paint thickness that could safely be applied on an aircraft skin with very low risk of puncture due to lightning is still a controversial matter. This paper introduces the analysis and investigation performed by the Bombardier Aerospace Core Engineering Electromagnetics team through several test campaigns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".