A Novel Approach for Technology Development: A Success Story
Bibliographic record
Abstract
The composites development team at Bombardier Aerospace has pushed the Integrated Product Development Team to a new level. The team has been created outside the business priorities and was partially funded by a provincial government initiative to create a greener aircraft. A dedicated R&D team can reduce the gap between the different disciplines by encouraging them to work as one entity and rapidly develop high Technology Readiness Level (TRL) and high Manufacturing Readiness Level (MRL) solutions. Additionally, the interactions between the groups create a harmonization of the development philosophy and a sharing of the building block approach. This leads to a significant cost and lead time reduction in the coupon, element and detail testing. The constitution of the team also has a great impact on the level of expertise and the flexibility to adjust to new demands. The team has built through the years an entire and complete external partner network that helps the team in specific subject matters. Knowledge management within the team, technology development and sharing become critical aspects as the projects grow. To ensure that all the necessary knowledge is captured throughout the development, a rigorous process is implemented in the IPDT team. The process to identify and close knowledge gaps requires a knowledge capture technique, named A3 process, adapted to the R&D needs. The A3 is used in a loop approach where specific sub-subjects of a project are each tracked with this methodology.
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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".