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Record W2905686003 · doi:10.1002/anie.201812534

High Throughput Strategies for the Discovery and Optimization of Catalytic Reactions

2018· review· en· W2905686003 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

VenueAngewandte Chemie International Edition · 2018
Typereview
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Ontario
KeywordsThroughputComputer scienceCatalysisHigh-throughput screeningComputational biologyBiochemical engineeringData scienceChemistryBiologyEngineeringTelecommunicationsBiochemistry

Abstract

fetched live from OpenAlex

Homogeneous catalysis has provided chemists with numerous transformations to enable rapid construction of organic molecules. However, these reactions are complex, requiring multiple substrate-dependent mechanistic steps to operate in harmony under a single set of experimental conditions. As a consequence, synthetic chemists often carry out laborious, empirical screening to identify suitable catalysts, solvents, and additives to achieve high yields and selectivity. In this Minireview, recently developed tools, technologies, and strategies will be described that improve this development process. In particular, the application of high throughput techniques to run more experiments, experimental design principles to access better data, and statistical tools to provide predictive models will be discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.298
Teacher spread0.267 · 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