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Record W2338480564 · doi:10.1021/acs.joc.6b00691

Computations Reveal That Electron-Withdrawing Leaving Groups Facilitate Intramolecular Conjugate Displacement Reactions by Negative Hyperconjugation

2016· article· en· W2338480564 on OpenAlexaff
Elizabeth L. Noey, Gonzalo Jiménez‐Osés, D. J. L. CLIVE, K. N. Houk

Bibliographic record

VenueThe Journal of Organic Chemistry · 2016
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Alberta
FundersDivision of ChemistryNational Science Foundation
KeywordsChemistryIntramolecular forceHyperconjugationConjugateDisplacement (psychology)Computational chemistryPhotochemistryStereochemistryOrganic chemistryMoleculePsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Intramolecular conjugate displacement (ICD) reactions, developed by the Clive group, form carbocycles and polycyclic amines by intramolecular nucleophilic attack on a Michael acceptor with an allylic leaving group. Quantum mechanical investigations with density functional theory show that ICDs involve a stepwise addition, forming an intermediate stabilized carbanion, followed by elimination. The electron-withdrawing nature of the allylic leaving group facilitates the addition by negative hyperconjugation; the twist-boat conformation of the addition and intermediate is stabilized by this interaction. In the absence of an activating electron-withdrawing group as part of the Michael acceptor, a high energy concerted SN2' reaction occurs. The reactions of carbon nucleophiles have lower activation energies than those of amines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.006
GPT teacher head0.192
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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