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Functional Group Oxidation and Reduction

2017· book-chapter· en· W4230932119 on OpenAlexaboutno aff
Douglass F. Taber

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook-chapter
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsAldehydeCatalysisChemistryNitrileAlkeneAlkyneAlcoholOrganic chemistryMedicinal chemistry

Abstract

fetched live from OpenAlex

Christophe Darcel and Jean- Baptiste Sortais of the CNRS-Université Rennes 1 reduced (Chem. Commun. 2013, 49, 10010) an acid 1 to the aldehyde 2 with a Mn catalyst under photostimulation. The same authors also used (Angew. Chem. Int. Ed. 2013, 52, 8045) an Fe catalyst to reduce an ester (not illustrated) to the corresponding aldehyde. Yasushi Tsuji of Kyoto University employed (Adv. Synth. Catal. 2013, 355, 3420) a Pd catalyst to reduce acid to the aldehydes. Chao-Jun Li of McGill University found (Angew. Chem. Int. Ed. 2013, 52, 11871) that a Ag catalyst in water would reduce an aldehyde 3 to the alcohol 4. Ketones were not reduced under these conditions. David Milstein of the Weizmann Institute of Science devised (Angew. Chem. Int. Ed. 2013, 52, 14131) an Fe catalyst for the E-selective reduction of an alkyne 5 to the alkene 6. Debabrata Maiti of IIT Bombay effected (Chem. Commun. 2013, 49, 8362) reductive cleavage of a nitrile 7 to the alkane 8. Aryl nitriles were also reduced. Professor Li used (Eur. J. Org. Chem. 2013, 6496) an Ir catalyst and hydrazine under H-transfer conditions to reduce an alcohol 9 to the hydrocarbon 10. Kenneth M. Nicholas of the University of Oklahoma reduced (Chem. Commun. 2013, 49, 8199) the diol 11 to the alkene 12 with a V catalyst. Qiang Liu of Lanzhou University and Li- Zhu Wu of the Technical Institute of Physics and Chemistry showed (Eur. J. Org. Chem. 2013, 7528) that irradiation in the presence of a photoredox catalyst and a Hantzsch ester removed the sulfonyl group of 13. Selective oxidation is a powerful tool for organic synthesis. Eike B. Bauer of the University of Missouri St. Louis oxidized (Chem. Commun. 2013, 49, 5889) the diol 15 to the ketone 16 with an Fe catalyst and 30% hydrogen peroxide. Yan- qin Yuan of Lishui University and Jiannan Xiang of Hunan University selectively (Org. Lett. 2013, 15, 4654) thiolated the ether 17 to 18, that has the aldehyde oxidation state. Chengjian Zhu of Nanjing University converted (Adv. Synth. Catal. 2013, 355, 3558) the aldehyde 19 to the thioester 20 by oxidation in the presence of diphenyl disulfide.

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.007

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 head0.028
GPT teacher head0.201
Teacher spread0.173 · 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
GenreOther

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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