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Record W2322333884 · doi:10.1515/gps-2013-0028

Zing Microwave and Flow Chemistry Conference (Napa Valley, USA, July 20–23, 2013)

2013· article· en· W2322333884 on OpenAlexfundno aff
Matthew Kirkby

Bibliographic record

VenueGreen Processing and Synthesis · 2013
Typearticle
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsnot available
FundersUniversidade Federal do Rio de JaneiroDurham UniversityLeibniz-GemeinschaftYork UniversityEli Lilly and Company
KeywordsFlow chemistryContinuous flowMicrowave chemistryChemistryGreen chemistryNanotechnologyEngineeringOrganic chemistryMicrowave irradiationBiochemical engineeringMaterials scienceCatalysisIonic liquid

Abstract

fetched live from OpenAlex

This conference brings together an outstanding group of academic and industrial speakers using microwave heating and/or continuous-flow processing in their chemistry platforms. It will be of interest to those who already use these tools in their setting as well as those interested in getting started. The focus of the conference will be on the application of these technologies to organic chemistry and will span production scales from milligrams to multiple kilograms per day. The use of microwave and continuous-flow processing for preparing a wide range of final products will be discussed, including medicinal chemistry, fine chemistry and bulk chemicals. An important part of the conference will be dissemination of new, previously unpublished results. Proposed Session Titles -Microwave chemistry in organic, peptide chemistry -Microwave chemistry in drug discovery/medicinal chemistry -Microwave and flow chemistry in nanomaterial ' s research -Flow chemistry in drug discovery -Flow photochemistry -Microreactor research -Process chemistry/continuous manufacturing -Microwave-assisted flow processing

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.150
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1500.059

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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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
Published2013
Admission routes1
Has abstractyes

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