Framework for the development of metrics for design for environment (DfE) assessment of products
Bibliographic record
Abstract
This paper considers issues facing companies with respect to translating environmental policy into an effective design for environment (DfE) program that can help a company move closer to a sustainability operating framework. Development and use of DfE have been discussed, and conceptual frameworks have been published by the Canadian Standards Association and others. This paper is intended to provide a framework for developing DfE metrics, drawing from the authors' experience in implementing DfE programs in several companies. This framework is not intended to be prescriptive nor absolute. Rather, it should provide development ideas and an understanding of the sequence of events that could help in developing effective DfE metrics.
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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 | 0.061 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".