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Record W2560250796 · doi:10.1021/jacs.6b10413

Identifying Cytosine-Specific Isomers via High-Accuracy Single Photon Ionization

2016· article· en· W2560250796 on OpenAlexfundno aff
Ziyong Chen, Kai‐Chung Lau, Gustavo A. García, Laurent Nahon, Dušan K. Božanić, Lionel Poisson, Muneerah Mogren Al‐Mogren, Martin Schwell, Joseph S. Francisco, A. Bellili, M. Hochlaf

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersInstitute of Population and Public HealthKing Saud University
KeywordsTautomerChemistryIonizationCytosineIonization energyPhotoionizationAb initioSpectral lineComputational chemistryDNAStereochemistryIonOrganic chemistry

Abstract

fetched live from OpenAlex

Biological entities, such as DNA bases or proteins, possess numerous tautomers and isomers that lie close in energy, making the experimental characterization of a unique tautomer challenging. We apply VUV synchrotron-based experiments combined with state-of-the-art ab initio methodology to determine the adiabatic ionization energies (AIEs) of specific gas-phase cytosine tautomers produced in a molecular beam. The structures and energetics of neutral and cationic cytosine tautomers were determined using explicitly correlated methods. The experimental spectra correspond to well-resolved bands that are attributable to the specific contributions of five neutral tautomers of cytosine prior to ionization. Their AIEs are experimentally determined for the first time with an accuracy of 0.003 eV. This study also serves as an important showcase for other biological entities presenting a dense pattern of isomeric and tautomeric forms in their spectra that can be investigated to understand the charge redistribution in these species upon ionization.

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 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 score0.010
Threshold uncertainty score0.922

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 integrity0.0000.000
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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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.

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

Citations33
Published2016
Admission routes1
Has abstractyes

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