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Record W2171017223 · doi:10.5194/angeo-22-1549-2004

Determining rotational temperatures from the OH(8-3) band, and a comparison with OH(6-2) rotational temperatures at Davis, Antarctica

2004· article· en· W2171017223 on OpenAlexaff
Frances Phillips, G. B. Burns, John French, P. F. B. Williams, Andrew Klekociuk, R. P. Lowe

Bibliographic record

VenueAnnales Geophysicae · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsWestern University
FundersAustralian Antarctic DivisionNational Science Foundation
KeywordsAirglowRotational temperatureSpectral lineAbsorption (acoustics)Intensity (physics)Absorption spectroscopyAbsorption bandAnalytical Chemistry (journal)Line (geometry)Atmospheric sciencesMaterials scienceAtomic physicsPhysicsChemistryOpticsAstronomyMathematics

Abstract

fetched live from OpenAlex

Abstract. Rotational temperatures derived from the OH(8–3) band may vary by ~18K depending on the choice of transition probabilities. This is of concern when absolute temperatures or trends determined in combination with measurements of other hydroxyl bands are important. In this paper, measurements of the OH(8–3) temperature-insensitive Q/P and R/P line intensity ratios are used to select the most appropriate transition probabilities for use with this band. Aurora, airglow and solar and telluric absorption in the OH(8–3) band are also investigated. Water vapour absorption of P1(4), airglow or auroral contamination of P1(2) and solar absorption in the vicinity of P1(5) are concerns to be considered when deriving rotational temperatures from this band. A comparison is made of temperatures derived from OH(6–2) and OH(8–3) spectra collected alternately at Davis (69° S, 78° E) in 1990. An average difference of ~4K is found, with OH(8–3) temperatures being warmer, but a difference of this magnitude is within the two sigma uncertainty limit of the measurements. Key words. Atmospheric composition and structure airglow and aurora; pressure, density, and temperature)

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.577

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 studies0.0010.000
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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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 designObservational
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

Citations24
Published2004
Admission routes1
Has abstractyes

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