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Record W2618364393 · doi:10.1021/acs.joc.7b00568

Synthesis of Pyridobenzazepines Using a One-Pot Rh/Pd-Catalyzed Process

2017· article· en· W2618364393 on OpenAlexafffund
Heather Lam, Jennifer Tsoung, Mark Lautens

Bibliographic record

VenueThe Journal of Organic Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicCyclopropane Reaction Mechanisms
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsChemistryCatalysisProcess (computing)Combinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In recent years, our group has been developing multicatalytic reactions for the synthesis of biologically relevant heterocyclic compounds. An efficient dual-metal catalyzed reaction of electron-deficient o -chlorovinylpyridines with o -aminophenylboronic esters to access pyridobenzazepines is described. Combining a Rh I -catalyzed arylation followed by a Pd 0 -catalyzed C–N coupling, in a one-pot procedure, provides a simplified method to access heterocycles without workup and purification after each step. The substrate scope encompasses a variety of N -H and N -alkylated pyridobenzazepine variants with yields up to 93%.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.274
Teacher spread0.247 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations15
Published2017
Admission routes2
Has abstractyes

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