Predicting the reactivity of ambidentate nucleophiles and electrophiles using a single, general-purpose, reactivity indicator
Bibliographic record
Abstract
We recently proposed a new reactivity indicator, termed the "general-purpose reactivity indicator", Xi, which describes not only the classical reactivity paradigms, but also describes reactions that are neither frontier-orbital nor electrostatically controlled. This indicator was proposed to be especially useful for reactants with multiple reactive sites, especially if the nature of the reactivity at those sites was different. This suggests that this reactivity indicator is especially appropriate for ambidentate molecules; this paper confirms this hypothesis. The general-purpose reactivity indicator not only identifies the most reactive sites, it also identifies which substrates prefer which reactive sites. In particular, the reactivity indicator allows one to clearly distinguish which sites of an ambidentate molecule are most reactive when electron transfer from the attacking reagent is large (a soft reagent) and which sites are most reactive when the attacking reagent is hard and highly charged (so that electron transfer is relatively insignificant). To illustrate the efficacy of the indicator for nucleophiles we consider SCN(-), SeCN(-), NO(2)(-), SO(3)(2-). For electrophiles we consider dimethyl carbonate, N-methyl-N-nitrosotoluene-p-sulfonamide (MNTS), and 1-chloro-2,4,6-trinitrobenzene (CNB).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".