Bibliographic record
Abstract
Design for multi-lifecycle (DFML) is a sustainable design approach that seeks to maximize the utility of resources used in developing a product by incorporating features that enable the elongation of the techno-economic service life of that product at the design stage. The goal of DFML is “indefinite” use of the resources invested/embodied in a product without compromising its economic value, technological soundness and socio-cultural acceptability. However, there is a limit to how many times a product designed for multi-lifecycle can be cycled. The aim of this research is to identify issues affecting how many times products designed for multi-lifecycle could be cycled. Another goal of this study is to articulate how the understanding of these issues can be utilized in improving product design for multi-lifecycle. This study is based on intensive literature survey and on over twenty years’ experience in conventional- and in sustainable design and development of agri-food machinery. From the study we learned that the sustainability of products and equipment designed for multi-lifecycle depends, among other things, on the durability of the core components, the required performance standard, resource consumption tipping point, economic advantage eradication point, changes in consumer taste, and regulatory changes. It means that the number of times that resources invested in a product designed for multi-lifecycles can be cycled is increasable by improving the durability of the structure and core components of the product. It also means that designers would be able to improve the sustainability of machinery designed for multi-lifecycles by incorporating features that facilitate easy reconfiguration and upgrading of the product at reasonable cost as consumer taste and regulations changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".