Driving Design Towards a Sustainable Aviation Industry Product Using Environmental Impact Evaluation
Bibliographic record
Abstract
The goals of sustainable manufacturing, as articulated by Organization for Economic Co-operation and Development (OECD), are to reduce the intensity of material use, energy consumption, emissions and unwanted by-products - while maintaining or improving the value of products to society and to organizations. Benefits that can be achieved through this practice include improved working conditions, public image, staff morale, customer loyalty, brand value, profits, sales turnover, product performance, reduction in waste generation and staying ahead of regulatory concerns. Achieving such goals begins early in the product design phase with consideration toward materials used and processes invoked in manufacturing. Ultimately, a full sustainability assessment must include the product's End-of-life (EOL) impact, factoring environmental impacts of landfill and recycling emissions. This paper focuses on using an End-Of-Life impact assessment for a set of materials and processes commonly used in the aerospace industry. Available data and best practices are used to forecast the final EOL impact of an aerospace product for a given set of materials and processes. The approach is able to quantify costs incurred to advance manufacturing processes and can be used to inform top management on sustainability decisions. The approach could be extended to assess the complete aircraft, including its Beginning of Life.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".