A Conceptual Framework for Value Chain Analysis of End of Life Aircraft Treatment in the Context of Sustainable Development
Bibliographic record
Abstract
The End of Life phase of Aircraft is a relatively complex phase in life cycle of this product. The retired Aircrafts need to be parked in certain conditions. Some valuable parts are disassembled and the rest of them are dismantled. Materials are separated and upgraded, waste is burned or deserted and toxic materials restrained or incinerated. All of these activities should be performed in an ecologically right manner; however, collectively produced added values for all stakeholders need to be considered. This paper aims to provide a conceptual framework for value chain analysis of Aircraft recycling process in the context of sustainable development. The value chain related to recycling aircraft at the end of life was chosen to generate an in-depth analysis of the value chain, considering environmental and socio-economic concerns. The value chain framework for recycling of fleets is identified. The key processes with environmental and social impacts are determined. The decision making process along the value chain and the policy framework including codes, regulations and standards are addressed. Finally, the proposed approach with providing a basis for evaluation of effectiveness, efficiency and stability across the value chain aids decision makers to design a sustainable framework for End of Life aircrafts treatment.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".