Metrics for green syntheses
Bibliographic record
Abstract
Abstract Despite being introduced approximately 30 years ago, green metrics are still not widely implemented in the practice of Green Chemistry. Nowadays, there is a general desire and fashion for Green Chemistry considering the modern global concerns of climate change and resource scarcity. However, the scientific literature reveals a confusing array of definitions and methodologies related to green metrics, particularly in both organic and inorganic chemistry. In this review we want to focus on organic synthesis, namely new reaction pathways that employ organic and inorganic catalysts, grounded in fundamental chemistry. The application of rigorous green metrics must go along with the experimental validation of synthetic procedures. This is essential to establish clear guidelines for defining truly green synthetic approaches, and to prevent misunderstandings or overreaching claims that are based on subjective rather than objective assessments. This work originated from an IUPAC project aimed at providing standardized guidance for the use of green metrics. Accordingly, we present a list of green metrics and related terminology currently employed to assess material usage, energy efficiency, and environmental impact in individual reactions and synthetic strategies.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".