A life cycle design method based on green feature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current methods of life cycle assessment mainly focus on the impacts evaluation of existing products and detailed design of products, and there is a lack of direct guidance to the green design process. In this paper, a new life cycle design method based on green features is proposed for mechanical and electrical products. Firstly, the concept of green feature is proposed to describe information of green design. Then, through exploration of the mapping relationship between traditional design information and green feature, the information expression model which is easy to extract green features is established. Lastly, the LCA idea is introduced to design process and is combined with green feature, thus the green features in traditional design are identified, selected, classified and aggregated and the green feature-based modeling can be realized. The feedback of rapid life cycle assessment results can be conducted real time and green design is embedded into traditional design, which can improve environmental attributes of mechanical and electrical products. Finally, a case is used to illustrate the application of green feature.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it