Technology integration maturity assessment for aircraft development programs
Bibliographic record
Abstract
Technology integration maturity assessment for aircraft development programs Susan Liscouet-Hanke (1) 1 : Bombardier Aerospace, C.P. 6087, succ. Centre-Ville, Montreal, Qc, Canada, H3C 3G9, susan.liscouet-hanke@aero.bombardier.com Abstract Technology Readiness Levels are the industry standard to assess technology maturity. New technology is often developed in separated technology bricks. The presentation addresses an approach to define operationally and assess the maturity of the integration of these technology bricks into an aircraft platform, for the example of more electric aircraft technologies. Introduction The assessment of technology maturity regarding its readiness for implementation into a new or derivative aircraft development program is central to technology development initiatives and a key activity in aircraft conceptual design phase. Technology maturity is most widely assessed via the Technology Readiness Levels (TRL) scale defined in [1]. The interpretation of TRL can be ambiguous, especially when it comes to the important aspect of technology integration. Whereas, mastering the technology integration is a key success factor in order to develop new aircraft while re
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".