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Record W4205162439 · doi:10.3410/f.738877493.793582499

Faculty Opinions recommendation of Carboxyboronate as a Versatile In Situ CO Surrogate in Palladium-Catalyzed Carbonylative Transformations.

2021· dataset· en· W4205162439 on OpenAlexaboutno aff
Mahesh K. Lakshman

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2021
Typedataset
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsPalladiumArylChemistrySonogashira couplingCatalysisCombinatorial chemistryNucleophileHalideCarbonylationOrganic chemistryIn situCarbon monoxide

Abstract

fetched live from OpenAlex

The application of carboxy-MIDA-boronate (MIDA=N-methyliminodiacetic acid) as an in situ CO surrogate for various palladium-catalyzed transformations is described. Carboxy-MIDA-boronate was previously shown to be a bench-stable boron-containing building block for the synthesis of borylated heterocycles. The present study demonstrates that, in addition to its utility as a precursor to heterocycle synthesis, carboxy-MIDA-boronate is an excellent in situ CO surrogate that is tolerant of reactive functionalities such as amines, alcohols, and carbon-based nucleophiles. Its wide functional-group compatibility is highlighted in the palladium-catalyzed aminocarbonylation, alkoxycarbonylation, carbonylative Sonogashira coupling, and carbonylative Suzuki-Miyaura coupling of aryl halides. A variety of amides, esters, (hetero)aromatic ynones, and bis(hetero)aryl ketones were synthesized in good-to-excellent yields in a one-pot fashion.© 2020 Wiley-VCH GmbH. PMID: 33085182 Funding information This work was supported by: Connaught Fund, Natural Sciences and Engineering Research Council of Canada,

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)0.007
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0420.023

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 head0.023
GPT teacher head0.347
Teacher spread0.324 · 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
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFaculty Opinions – Post-Publication Peer Review of the Biomedical LiteratureSame topicCatalytic Cross-Coupling ReactionsFrench-language works237,207