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Record W2137228792 · doi:10.1139/v04-042

Highly accurate transition frequencies in the H<sub>2</sub> Lyman and Werner absorption bands

2004· article· en· W2137228792 on OpenAlexvenueno aff
Johan Philip, J. P. Sprengers, Th.G.P Pielage, C. A. de Lange, W. Ubachs, E. Reinhold

Bibliographic record

VenueCanadian Journal of Chemistry · 2004
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersEuropean Commission
KeywordsChemistrySpectroscopyIonizationSpectral lineAtomic physicsAnalytical Chemistry (journal)Absorption (acoustics)Absorption spectroscopyWavelengthLaserOpticsPhysicsIon

Abstract

fetched live from OpenAlex

The transition frequencies of low-J excitation lines in the B 1 Σ u + – X 1 Σ g + ,(ν′,0) Lyman bands have been measured for ν′ = 2–18 and in the C 1 Π u –X 1 Σ g + ,(ν′,0) Werner bands for ν′ = 0–4, using a narrow-band tunable extreme UV laser source at wavelengths λ = 92–105 nm in conjunction with the technique of 1 + 1′ two-photon ionization. The measurements can be divided into three categories for which varying absolute accuracies were obtained. Special focus was on the B, ν′ = 2–5 bands, where an accuracy of 0.004 cm –1 or δv/v = 4 x 10 –8 is achieved. For transitions to B, ν′ ≤ 13 and C, ν′ ≤ 3 states the accuracy is 0.005 cm –1 or δv/v = 5 x 10 –8 . Due to a different frequency mixing scheme uncertainties for B, ν′ ≥ 13 and C, ν′ = 4 are at the level of 0.011 cm –1 or δv/v = 1.1 x 10 –7 . Inspection of combination differences between R(J) and P(J + 2) lines shows that the accuracies are even better than estimated in the error budget. Based on the measurements of 138 spectral lines and the known combination differences, transition frequencies of 60 P-lines could be calculated as well, so that a data base of 198 accurately calibrated lines results for the Lyman and Werner bands of H 2 . Key words: vacuum UV, molecular spectroscopy, hydrogen, precision metrology.

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 score0.010
Threshold uncertainty score0.375

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 head0.009
GPT teacher head0.209
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 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

Citations50
Published2004
Admission routes1
Has abstractyes

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