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Record W3190743048 · doi:10.1039/d1cs00380a

Green chemistry meets medicinal chemistry: a perspective on modern metal-free late-stage functionalization reactions

2021· review· en· W3190743048 on OpenAlexafffund
Juan D. Lasso, Durbis J. Castillo‐Pazos, Chao‐Jun Li

Bibliographic record

VenueChemical Society Reviews · 2021
Typereview
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaCanada Research ChairsCanada Foundation for Innovation
KeywordsSurface modificationChemistryNanotechnologyCombinatorial chemistryMaterials sciencePhysical chemistry

Abstract

fetched live from OpenAlex

)-H functionalizations, as well as the utilization of heteroatom-containing functional groups as chemical handles. Selected topics such as metal-free cross-dehydrogenative coupling (CDC) reactions, organocatalysis, electrochemistry and photochemistry are also discussed. By writing the first review on metal-free LSF methodologies, we aim to highlight current advances in the field with examples that reveal specific challenges and solutions, as well as future research opportunities.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.085
GPT teacher head0.366
Teacher spread0.281 · 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
GenreReview

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

Citations150
Published2021
Admission routes2
Has abstractyes

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