Unification of Reaction Metrics for Green Chemistry II: Evaluation of Named\nOrganic Reactions and Application to Reaction Discovery
Bibliographic record
Abstract
The concept of minimum atom economy (AE)<sub>min</sub> and maximum\nenvironmental impact factor <i>E</i><sub>max</sub> is introduced and applied to\na database of more than 400 named organic reactions by\nemploying generalized Markush structures as a means of\ngauging worst-case scenarios for waste production for chemical\nreactions. The scope of the method can be extended to evaluate\n“green” performances for any chemical reaction once all\nbyproducts are identified. From the inverse relationship connecting AE and <i>E</i><sub>mw</sub> (environmental impact factor based on\nmolecular weight) and an analogous one connecting RME\n(reaction mass efficiency) and <i>E</i> (Sheldon environmental impact\nfactor), a minimum value of AE or RME equal to the golden\nratio, 0.618 (or 61.8%), is suggested as a threshold metric for\ngauging the true “greenness” of reactions. The rationale for\nthis is that this condition is met when the magnitude of AE\nexceeds that of <i>E</i><sub>mw</sub> and similarly when RME exceeds <i>E</i>.\nProbabilities for achieving this condition are also determined\nfor several organic reactions, and general expressions for\nevaluating such probabilities as functions of a general threshold\nα between 0 and 1 are also derived. Reactions in the database\nare classified according to general reaction types, and each class\nis ranked according to the “golden” threshold metric. Patterns\nwith respect to atom economical efficiency revealed by this\nanalysis are discussed, including the introduction of expressions\nfor determining the viability of recycling waste byproducts back\nto reagents. It is shown that the database can be used in a\ndiversity-oriented approach to discover new multicomponent\nreactions (MCRs) by combinatorial analysis of Markush fragments. In this context the top seven most frequently appearing\nMarkush structures in the database yield 33 viable three-component MCRs of which 12 have literature precedence.\nSynthetic strategies for reaction design are put forward using\nthe optimum criteria suggested by analysis of the database.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".