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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0300.000

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.

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