Aircraft Eco-assembly: a strategy to reduce the ecological footprint of aerospace industry
Bibliographic record
Abstract
The aerospace industry is a vector and a destructive agent for the environment. It is observed several pollutants and harmful wastes in all aeronautical activities. Given the negative impacts of the aerospace industry on the environment, large organizations such as Airbus, Boeing, Bombadier and Embraer have taken awareness of the urgency to inhibit environmental footprint. It has become imperative to design flying machines that could be less harmful for the environment. Therefore, aerospace industry has undertaken initiatives to limit its environmental footprint for two main phases of the aircraft lifecycle, named utilisation and final disposal. For utilisation, eco-design was used to reduce energy dependency and reduce pollution rate. For final disposal, eco-design enables aircraft recycling or final disposal under legal and industry norms. However, our literature review highlights the gap for projects to reduce environmental footprint during aircraft assembly. This paper identifies the main sources of pollution during the aircraft assembly phase and evaluates the possible strategies or initiatives to be undertaken to reduce waste quantity and to ensure their management. These strategies are named in this paper as eco-assembly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".