DEVELOPMENT OF A DESIGN FOR END-OF-LI Development of a design for end-of-life approach in a strongly guided design process. Application to high-tech products.
Bibliographic record
Abstract
In response to a growing concern for environmental problems and to waste management from mass production products, several regulations have appeared to tackle end-of-life (EoL) issues. They address for instance end-of-life vehicles or waste electrical and electronic equipment. EoL management mainly lays on both EoL industry and product design. Thus, new methods of design have already been implemented since the past decades to answer the regulation requirements, notably through material choices and product architecture. However, some high-tech products remain out of the scope of these legislations. But for some years, initiatives have emerged for these products, coming from governments, international programs or customers’ requirements which become increasingly strict. This paper focuses on a new design approach that would allow taking into account EoL considerations for such type of products, based on EoL strategies and adapted to aeronautic and defence products.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".