Indices for predicting the quality of leaving groups
Why this work is in the frame
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Bibliographic record
Abstract
The inherent quality of leaving groups in chemical reactions is related to their ionization potential and electron affinity using a quadratic model for the dependence of the energy on the number of electrons. A good leaving group for nucleophilic substitution/elimination reactions is one where the difference in energy between the system with the "optimum" number of electrons and the anion is small. Similarly, a good leaving group for electrophilic substitution/elimination reactions is one where the difference in energy between the system with the optimum number of electrons and the cation is small. This insight allows us to define indices for the quality of leaving groups in nucleophilic and electrophilic reactivity, which we term the nucleofugality and the electrofugality, respectively. These indices are useful not only for predicting the quality of leaving groups in organic reactions, but also for explaining the stability of carbocations, carbanions, and trends in pKa.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it