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Record W2285036449 · doi:10.1366/14-07714

Determination of Spectroscopic Band Shapes by Second Derivatives, Part II: Infrared Spectra of Liquid Light and Heavy Water

2015· article· en· W2285036449 on OpenAlexaff
Jean-Joseph Ma×, Camille Chapados

Bibliographic record

VenueApplied Spectroscopy · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsInfraredRaman spectroscopyInfrared spectroscopySpectral lineContext (archaeology)Liquid waterAnalytical Chemistry (journal)ChemistryPrincipal component analysisAbsorption spectroscopyAbsorption bandAbsorption (acoustics)Spectral bandsDerivative (finance)Molecular physicsOpticsPhysicsChromatographyMathematicsGeologyThermodynamics

Abstract

fetched live from OpenAlex

Second derivative and band simulation techniques are used in a synergetic relationship to identify components in the infrared (IR) spectra of liquid light and heavy water. Nine Gaussian components are retrieved in massive OH and OD stretch absorption. In this context, ν1 and ν3 are the principal components along with satellites derived from harmonic and combination bands. The Raman spectrum of light water matches the IR components with intensity variations as expected.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
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)0.0010.000

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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
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

Citations6
Published2015
Admission routes1
Has abstractyes

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