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Record W2772285008 · doi:10.1021/acs.jpca.7b10303

Applying Machine Learning to Vibrational Spectroscopy

2017· article· en· W2772285008 on OpenAlexafffund
Weiqiang Fu, W. Scott Hopkins

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2017
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCluster (spacecraft)DimerSpectroscopyProtonationSpectral lineInfrared spectroscopyPotential energy surfacePartition (number theory)Electrospray ionizationHierarchical clusteringAtomic physicsCluster analysisMolecular physicsComputational chemistryMoleculeIonCombinatoricsArtificial intelligencePhysicsOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

The low-energy region of the potential energy surface (PES) of the protonated phenylalanine/serine dimer is mapped using the basin-hoping search algorithm, and 37 isomers are identified within 180 kJ·mol –1 of the global-minimum structure. Cluster structures are grouped using hierarchical clustering to partition the PES in terms of nuclear configuration. Calculated IR spectra for the various isomers are then compared with the isomer-specific IR spectra by means of the cosine distance metric to facilitate spectral assignment and identify which regions of the PES are populated in the electrospray ionization process.

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 categoriesInsufficient 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 score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.292
Teacher spread0.279 · 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

Citations56
Published2017
Admission routes2
Has abstractyes

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