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Reduction and Oxidation

2015· book-chapter· en· W3106240198 on OpenAlexaboutno aff
Douglass F. Taber

Bibliographic record

VenueOxford University Press eBooks · 2015
Typebook-chapter
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryAlkyneCatalysisPolymer chemistryAlkeneEpoxideMedicinal chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Craig M. Williams of the University of Queensland and John Tsanaktsidis of CSIRO Victoria decarboxylated (Org. Lett. 2011, 13, 1944) the acid 1 to the hydrocarbon 2 by coupling the crude acid chloride, formed in CHCl3, with 3 while irradiating with a tungsten bulb. In a related development, David C. Harrowven of the University of Southampton showed (Chem. Commun. 2011, 46, 6335, not illustrated) that tin residues can be removed from a reaction mixture by passage through silica gel containing 10% K2CO3. Sangho Koo of Myong Ji University selectively removed (Org. Lett. 2011, 13, 2682) the allylic oxygen of 5, leaving the other protected alcohol. Donald Poirier of Laval University reduced (Synlett 2011, 2025) the nitrile of 7 to a methyl group. Kiyotomi Kaneda of Osaka University prepared (Chem. Eur. J. 2010, 16, 11818; Angew. Chem. Int. Ed. 2011, 50, 2986) supported Au nanoparticles that deoxygenated an epoxide 9 to the alkene 10. Epoxides of cyclic alkenes also worked well. Shahrokh Saba of Fordham University aminated (Tetrahedron Lett. 2011, 52, 129) the ketone 11 by heating it with an amine 12 in the presence of ammonium formate. Shuangfeng Yin and Li-Biao Han of Hunan University devised (J. Am. Chem. Soc. 2011, 133, 17037) catalyst systems that reduced the alkyne 14 selectively to either the Z or the E product. Professor Kaneda uncovered (Chem. Lett. 2011, 40, 405) a reliable Pd catalyst for the hydrogenation (not illustrated) of an alkyne to the Z alkene. David R. Spring of the University of Cambridge established (Synlett 2011, 1917) biphasic reaction conditions for the conversion of 16 to the azide 18 that were compatible with the base-sensitive Fmoc protecting group. Noritaka Mizuno of the University of Tokyo developed (J. Org. Chem. 2011, 76, 4606) a Ru catalyst for the transformation of an alkyl azide 19 to the nitrile 20. Chi-Ming Che of the University of Hong Kong (Synlett 2011, 1174) and Philip Wai Hong Chan of Nanyang Technological University (J. Org. Chem. 2011, 76, 4894) independently oxidized an aldehyde 21 to the amide 22.

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)0.002
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 score0.026
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0260.015

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 head0.030
GPT teacher head0.209
Teacher spread0.179 · 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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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