Simplified Application of Material Efficiency Green Metrics to Synthesis Plans: Pedagogical Case Studies Selected from <i>Organic Syntheses</i>
Bibliographic record
Abstract
This paper presents a simplified approach for the application of material efficiency metrics to linear and convergent synthesis plans encountered in organic synthesis courses. Computations are facilitated and automated using intuitively designed Microsoft Excel spreadsheets without invoking abstract mathematical formulas. The merits of this approach include (a) direct application of green chemistry principles to synthesis planning; (b) strongly linking green metrics calculations and synthesis strategy; (c) pinpoint identification of strengths and weaknesses of any synthesis plan’s material efficiency performance using effective visual aids; (d) in-depth quantitative and qualitative critiquing of synthesis plan performance and strategy; and (e) giving opportunities to students to offer insightful suggestions to improve or “green up” published procedures based on their growing personal database of chemical reactions as they continue their education in chemistry. An extensive database of over 600 examples taken from Organic Syntheses was created as a repository of reliable examples that instructors can draw upon to create meaningful classroom pedagogical exercises and homework problem sets that couple material efficiency green metrics analyses and traditional learning of organic chemistry.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".