Sustainability Tools II – Impact-based Metrics
Bibliographic record
Abstract
This chapter guides instructors in teaching students to identify the least harmful chemical options using impact-based metrics rather than mass-based metrics. It argues for the need to move away from mass-based metrics, such as atom economy and E-factor, because these do not consider the potential harm caused by chemicals and waste and cannot evaluate environmental impact. Teaching students to consider quantitative impact metrics rather than rely on popular misconceptions or generalizations can give them the tools to make informed decisions about what is green or sustainable. This decision-making ability is needed so that students can put into action the key goal of green chemistry: to reduce harm to health and the environment from chemicals. This chapter outlines a strategy and learning outcomes for integrating impact metrics and the general idea of quality over quantity across all levels of the chemistry post-secondary curriculum. Furthermore, it acknowledges and provides solutions to the challenges in integrating Life Cycle Assessment (LCA) into undergraduate chemistry courses, concluding with activities and practical examples.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".