In-Service Information for Aeroengine Designers: A Survey
Bibliographic record
Abstract
One of the primordial aspects of the integrated product service paradigm is efficient flow and sharing of information between the stakeholders during the entire life of a product. This paper presents an improved understanding of the nature of the product in-use information required by aircraft design engineers. The findings are based on a vast survey which expands on a previous qualitative study reported in the literature. The results presented here are therefore a much needed quantitative measure of the in-service information requirements at the aircraft design stage. The survey has helped the authors to depict a system of in-service information feedback to designers which can be qualified as informal in nature, producing inefficient or frustrating results. Indeed, the feedback process is established mainly through personal contacts and does not necessarily ensure that appropriate, complete information is systematically available in a timely manner for designers. On the bright side, the responses from the participants consolidate the belief that the required information does exist within heterogeneous databases and that designers recognize the importance of in-service information to their work. A formalized in-service information feedback process is therefore seen as one of the strategic mechanisms that needs to be implemented to ensure proper product development and service integration.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".