Bibliographic record
Abstract
An approach to reduce certification development cost can be accomplished by testing the important strength allowables that make up the primary design drivers. Through a process referred to as a “modified building block approach,” these primary design drivers are weighted and selected based on criticality. This approach was used to minimize the development cost of a prototype composite tailboom being considered for a light model helicopter (similar in size to a Bell Model 407). The material system of choice (AS4/APC-2 thermoplastic) and the tailboom's susceptibility to impact damage drove the need to understand impact damage and its effect on strength. Compression after impact (CAI) at barely visible impact damage (BVID) was therefore selected as the critical design parameter. The cost benefit is realized by focusing on the critical design parameters; thus the number of coupon tests necessary to support full aircraft development can be significantly reduced because only limited design aspects have to be considered. The critical design drivers selected would not necessarily be applicable to the fuselage or rotor blades, but they do provide a means to optimize the tailboom design while minimizing the development cost. Certification would still be verified by full-scale testing.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".