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Record W3127984306 · doi:10.1039/d0cc08389e

Cross-coupling reactions with esters, aldehydes, and alcohols

2021· article· en· W3127984306 on OpenAlexafffund
Yan‐Long Zheng, Stephen G. Newman

Bibliographic record

VenueChemical Communications · 2021
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceCanada Research Chairs
KeywordsCoupling reactionChemistryCoupling (piping)Organic chemistryCombinatorial chemistryComputational chemistryMaterials scienceCatalysis

Abstract

fetched live from OpenAlex

Cross-coupling reactions to form biaryls and π bond addition reactions to prepare substituted carbonyls or alcohols represent two of the most frequently performed families of chemical reactions. Recent progress in catalysis has uncovered substantial overlap between these two seemingly distinct topics. In particular, esters, aldehydes, and alcohols have been shown to act as carbon-based coupling partners in a range of Ni- and Pd-catalyzed reactions to prepare amides, ketones, substituted alcohols, alkanes, and more. These reactions provide promising alternatives to commonly used stoichiometric or multi-step reaction sequences. In this feature article, a selection of these transformations will be discussed with an emphasis on the key mechanistic steps that allow these non-traditional substrates to be incorporated into cross-coupling-like catalytic cycles.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.033
GPT teacher head0.319
Teacher spread0.286 · 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 designNot applicable
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

Citations47
Published2021
Admission routes2
Has abstractyes

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