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Record W2313484380 · doi:10.15278/isms.2015.tb07

HYDROGEN AND NITROGEN BROADENED ETHANE AND PROPANE ABSORPTION CROSS SECTIONS

2015· article· en· W2313484380 on OpenAlexaff
Robert J. Hargreaves, P. F. Bernath, Brant Billinghurst, Dominique Appadoo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsCanadian Light Source (Canada)
Fundersnot available
KeywordsPropaneSynchrotronHydrogenSpectral lineSpectrometerAnalytical Chemistry (journal)Infrared spectroscopyInfraredAbsorption (acoustics)HeliumAbsorption spectroscopyOuter planetsMaterials scienceNitrogenWater vaporChemistryAtomic physicsOpticsPlanetPhysics

Abstract

fetched live from OpenAlex

High-resolution infrared absorption cross sections are presented for the ν 9 band of ethane (C 2 H 6 ) at 823 cm -1 .These cross sections make use of spectra recorded at the Australian Synchrotron using a Fourier transform infrared spectrometer with maximum resolution of 0.00096 cm -1 .The spectra have been recorded at 150, 120 and 90 K for hydrogen and nitrogen broadened C 2 H 6 .They cover appropriate temperatures, pressures and broadening gases associated with the atmospheres of the Outer Planets and Titan, and will improve atmospheric retrievals.The THz/Far-IR beamline at the Australian Synchrotron is unique in combining a high-resolution Fourier transform spectrometer with an 'enclosive flow cooling' (EFC) cell designed to study molecules at low temperatures.The EFC cell is advantageous at temperatures for which the vapor pressure is very low, such as C 2 H 6 at 90 K.Hydrogen broadened absorption cross sections of propane between 700 and 1200 cm -1 will also be presented based on spectra obtained at the Canadian Light Source.

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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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