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Record W2504647545 · doi:10.1002/ajoc.201600246

Enol and Ynol Surrogates: Promising Substrates for Hypervalent Iodine Chemistry

2016· article· en· W2504647545 on OpenAlexafffund
Antoine Jobin‐Des Lauriers, Claude Y. Legault

Bibliographic record

VenueAsian Journal of Organic Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesCentre in Green Chemistry and CatalysisUniversité de Sherbrooke
KeywordsHypervalent moleculeEnolChemistryContext (archaeology)TautomerReactivity (psychology)IodineReagentKetoneOrganic chemistryCombinatorial chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract In numerous iodine(III)‐mediated methodologies that involve ketone compounds, the enol tautomer is expected to be the reactive species. In this context, the exploration of enol and ynol surrogates as substrates is of great interest. Activated π‐systems have been shown to exhibit interesting and highly exploitable behavior toward hypervalent iodine reagents. This has led to the development of numerous useful oxidative transformations. Enamines, enamides, enol derivatives, haloalkenes, and haloalkynes are all enol or ynol surrogates that are reactive towards the most popular iodanes and iodonium salts. This Focus Review will describe past and on‐going research involving these substrates to gain insight into the similarities and disparities observed in their reactivity profiles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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 designBench or experimental
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

Citations26
Published2016
Admission routes2
Has abstractyes

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