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Record W1489563720 · doi:10.1002/9780470749593.hrs017

Indeterminacies of Fitting Parameters in Molecular Spectroscopy

2011· other· en· W1489563720 on OpenAlexaff
J. K. G. Watson

Bibliographic record

Venuenot available
Typeother
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsDiatomic moleculeDegenerate energy levelsHamiltonian (control theory)Polyatomic ionEigenvalues and eigenvectorsSpectral lineDegrees of freedom (physics and chemistry)Series (stratigraphy)PhysicsQuantum mechanicsClassical mechanicsMoleculeChemistryComputational chemistryMathematics

Abstract

fetched live from OpenAlex

Abstract Analyses of molecular spectra often use the idea of an effective Hamiltonian , in which the dynamical variables are the degrees of freedom involved in the spectrum, and the effects of the other degrees of freedom are represented by the values of various parameters. However, it may not be possible to determine all these parameters by empirical fits of the spectra because different parameters may make indistinguishable contributions to the eigenvalues of the Hamiltonian. This article reviews a number of examples of such indeterminacies, including applications to vibration–rotation spectra of diatomic molecules, without or with corrections for the breakdown of the Born–Oppenheimer approximations; electron‐spin structure in diatomic molecules; centrifugal distortion in asymmetric‐top molecules; vibration–rotation resonances in polyatomic molecules; vibration–rotation interactions in degenerate vibrational states; and internal rotation. The discussion of these indeterminacies involves unitary transformations that are expanded as infinite series. This procedure may break down if the convergence of the series is slow. The study of near‐singularities of least‐squares matrices associated with the presence of indeterminacies is also discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0140.000

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 head0.249
Teacher spread0.237 · 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.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations12
Published2011
Admission routes1
Has abstractyes

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