Not Just an Academic Exercise: Systems Thinking Applied to Designing Safer Alternatives
Bibliographic record
Abstract
For the last seven years, an interdisciplinary course known as Greener Solutions, offered by the University of California, Berkeley Center for Green Chemistry, has brought together graduate students in chemistry, environmental health, and engineering to understand each other’s disciplines, and to work together to develop safer alternatives to hazardous chemicals and manufacturing processes. Through the course, interdisciplinary teams of UC Berkeley students have worked with partner organizations to identify safer alternatives to chemicals of concern, including investigating safer preservatives in personal care products, nonfluorinated durable water-repellant coatings for outerwear, and safer cross-linkers to replace formaldehyde in permanent press textiles and diisocyanates in spray polyurethane foam insulation. Students undertake a bioinspired design process and then assess the potential health and environmental hazards associated with each of their proposed alternatives relative to hazards of the current chemistries. The students generate a focused alternatives assessment that considers technical performance, relative hazard and exposure potential, and feasibility, creating an “opportunity map” for the partner company and, ideally, the industry sector as a whole. The Greener Solutions model for interdisciplinary, inquiry-based learning is training a new generation of chemists and engineers in a systems approach to design: one that more fully considers the health and environmental implications of chemical and material choices. An adaptation of the Greener Solutions course model to serve undergraduate civil engineering students at University of Victoria, B.C. demonstrates how the course elements can serve a different subject matter and instructional level.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".