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Record W2102784404 · doi:10.1063/1.4896569

Using experimental data and a contracted basis Lanczos method to determine an accurate methane potential energy surface from a least squares optimization

2014· article· en· W2102784404 on OpenAlexafffund
Xiaogang Wang, Tucker Carrington

Bibliographic record

VenueThe Journal of Chemical Physics · 2014
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsLanczos resamplingRoot mean squareAb initioPotential energy surfaceBasis (linear algebra)MethaneLanczos algorithmSurface (topology)Standard deviationEnergy (signal processing)Absolute deviationLeast-squares function approximationMathematicsChemistryPhysicsMaterials scienceAtomic physicsStatisticsGeometryEigenvalues and eigenvectorsQuantum mechanics

Abstract

fetched live from OpenAlex

We obtain an accurate methane potential energy surface (PES) by starting with the ab initio PES of Schwenke and Partridge [Spectrochim. Acta A 57, 887 (2001)] and adjusting 5 of their parameters to reproduce 40 reliable experimentally determined vibrational levels of CH4. The 40 levels include all 35 levels in polyads up to and including the Octad and 5 levels in the Tetradecad. The Tetradecad levels are obtained from direct experimental transitions. The fit reduces the root mean square deviation of these 40 levels from 4.80 cm(-1) to 0.28 cm(-1). The new PES ought to aid in the analysis of the Tetradecad. To further test the accuracy of the new PES, vibrational levels are computed for CH4, CH3D, CHD3, and (13)CH4 and are compared with the extensive experimental data. The errors are all within about 1 cm(-1) except for a few cases.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.044
GPT teacher head0.339
Teacher spread0.294 · 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 designSimulation or modeling
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

Citations65
Published2014
Admission routes2
Has abstractyes

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