Bridging aeronautics and aerospace (A&A) industrial practice and academic research via systematic product lifecycle management
Bibliographic record
Abstract
T products in the A&A industry have become more complex than ever before as have the related processes, such the A380 or Boeing 787 airplanes. The product lifecycle management concept has been broadly addressed as a holistic approach to connect product and process modeling domains so that the industry can take full advantage of engineering informatics supported by modern information and computer technologies (ICTs). However, the A&A industry and the related academic research are not interfaced well enough to support healthy development cycles. It can be appreciated that the industry demands coherent answers to address those “big-picture” thematic problems instead of just the “micro” solutions currently on offer. Formulating systematic research programs with highly specified interfaces among research “nuggets” is the promising approach of governments, corporations, and clusters of smalland medium-sized players. Researchers, on the other hand, should identify their works with the “local coordinates” of a bigger picture driven by the industry and should constantly adapt their individual solutions such that they are always ready to be integrated seamlessly with other collaborative solutions. The interfacing edge definition of the puzzle, i.e. semantics standardization, is the imperative research task for both the industry and the related academic research circle. The proposed session is to develop some common understanding and explore the new challenges for the next step research in both the industrial applications and academic research fields. Yongsheng Ma, J Appl Mech Eng 2013, 2:3 http://dx.doi.org/10.4172/2168-9873.S1.002
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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".