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Record W2906211192 · doi:10.1021/acs.organomet.8b00774

Catalytic Homogeneous Asymmetric Hydrogenation: Successes and Opportunities

2018· article· en· W2906211192 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

VenueOrganometallics · 2018
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCatalysisHomogeneousAsymmetric hydrogenationHomogeneous catalysisCatalytic hydrogenationOrganic chemistryCombinatorial chemistryEnantioselective synthesis

Abstract

fetched live from OpenAlex

This is an overview of successes in the realm of catalytic homogeneous asymmetric hydrogenation of substrates primarily of interest in the synthesis of pharmaceuticals in order to identify important problems still unsolved. First, tables are provided that list the successful reductions to over 90% enantiomeric excess of prochiral ketones to alcohols, imines to amines, and olefins to saturated carbon centers. Noted in the tables are the metal (including “green” metals Mn, Fe, and Co) or enzyme, the class of ligand, the conditions of the medium, and the scale of reduction, if over 1 kg of product, as well as the nature of the process, whether direct hydrogenation using H2 gas (DH), transfer hydrogenation (TH), or hydrogenation with dynamic kinetic resolution (DKR). Tables of representative pharmaceutical or fine chemicals products are provided for each class of substrate. With this overview, the opportunities for further research and development become clearer.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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