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Record W4200375396 · doi:10.1002/cmtd.202100068

Pressurized Sample Infusion

2021· article· en· W4200375396 on OpenAlexaff
Gilian T. Thomas, Sofia Donnecke, Ian C. Chagunda, J. Scott McIndoe

Bibliographic record

VenueChemistry - Methods · 2021
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsElectrospray ionizationSimple (philosophy)Process engineeringSample (material)Mass spectrometryComputer scienceScale (ratio)MoistureIonizationChemistryChromatographyEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Pressurized sample infusion (PSI) is a simple and effective means of continuously introducing a solution to an electrospray ionization mass spectrometry (ESI‐MS) source. It allows for acquisition of real‐time data in an air‐ and moisture‐free environment and requires minimal additional infrastructure. It is applicable for use for any reaction in which one or more components are detectable by ESI‐MS and for which time‐course information is desired over a time scale of seconds to minutes. The strengths and weaknesses of the method are critically examined, and technique tips and tricks are provided to enable maximal effectiveness when employing this approach to continuous monitoring of complex reaction mixtures.

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 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
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)0.0580.000

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 head0.028
GPT teacher head0.367
Teacher spread0.339 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreMethods

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

Citations29
Published2021
Admission routes1
Has abstractyes

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