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Record W1514711093 · doi:10.1063/1.2756537

Infrared spectra of helium clusters seeded with nitrous oxide, HeN4–N2O, with N=1–80

2007· article· en· W1514711093 on OpenAlexaff
A. R. W. McKellar

Bibliographic record

VenueThe Journal of Chemical Physics · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsHeliumSpectral lineInfraredAnisotropyInfrared spectroscopyAtomic physicsSupersonic speedMolecular physicsMoleculeIntermolecular forceChemistryAnalytical Chemistry (journal)Materials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

High resolution infrared spectra of HeN-N2O clusters are studied in the 2200 cm(-1) region of the N2O nu1 fundamental band. The clusters are produced in a pulsed supersonic jet expansion from a cooled nozzle source and probed using a tunable diode laser operating in a rapid-scan mode. Three isotopic forms are used (14N14N16O, 15N14N16O, and 15N15N16O) in order to support the spectral analyses. For clusters up to N approximately 24, the individual spectra are resolved, assigned, and analyzed together with complementary microwave data. Assignments for larger clusters are uncertain due to overlapping transitions, but an approximate analysis is still possible for N approximately 25-80. Compared to helium clusters containing the related CO2 or OCS molecules, the rotational dynamics of HeN-N2O clusters show similarities but also important differences. In particular, HeN-N2O has more irregular behavior in the range of N=6-17, indicating that conventional molecular structure plays a greater role. In general terms, these differences can be attributed to a greater degree of angular anisotropy in the He-N2O intermolecular potential.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.211
Teacher spread0.205 · 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

Citations30
Published2007
Admission routes1
Has abstractyes

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