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Record W2766458912 · doi:10.1063/1.4986037

An apparatus for quantitative high-harmonic generation spectroscopy in molecular vapours

2017· article· en· W2766458912 on OpenAlexfundno aff
Felicity McGrath, Allan S. Johnson, Dane R. Austin, Peter G. Hawkins, David Wood, Lukas Miseikis, Emma R. Simpson, Marta Castillejo, Ricardo Torres, Susan Parker, Thomas Siegel, J. P. Marangos

Bibliographic record

VenueReview of Scientific Instruments · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsnot available
FundersH2020 European Research CouncilEngineering and Physical Sciences Research CouncilGovernment of CanadaMinisterio de Economía y CompetitividadNatural Sciences and Engineering Research Council of CanadaEuropean CommissionResearch Councils UKHorizon 2020 Framework Programme
KeywordsVapoursSpectroscopyMaterials scienceHarmonicBar (unit)Phase (matter)High harmonic generationAnalytical Chemistry (journal)Jet (fluid)Molecular physicsOpticsLaserMechanicsPhysicsChemistryAcousticsChromatography

Abstract

fetched live from OpenAlex

We present an apparatus for performing gas phase high-harmonic generation spectroscopy of molecules primarily found in the liquid phase. Liquid molecular samples are heated in a temperature controlled bath and their vapour is used to back a continuous flow gas jet, with vapour pressures of over 1 bar possible. In order to demonstrate the system, we perform high harmonic spectroscopy experiments in benzene with a 1.8 μm driving field. Using the unique capabilities of the system, we obtain spectra that are nearly free from the effects of longitudinal phase-matching, amenable to comparison with advanced numerical modelling.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.191
Threshold uncertainty score0.350

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 communication0.0000.000
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 head0.035
GPT teacher head0.365
Teacher spread0.330 · 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.

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

Citations6
Published2017
Admission routes1
Has abstractyes

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