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Record W4403011227 · doi:10.1002/jms.5096

Where You Protonate Matters: Deciphering the Unimolecular Chemistry of Protonated Myrcene and Linalool

2024· article· en· W4403011227 on OpenAlexafffund
Edgar White Buenger, P. Mayer

Bibliographic record

VenueJournal of Mass Spectrometry · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du Canada
KeywordsChemistryProtonationFragmentation (computing)MyrcenePhotochemistryComputational chemistryMedicinal chemistryOrganic chemistryIon

Abstract

fetched live from OpenAlex

The unimolecular reactions of protonated myrcene and linalool were investigated by collision-induced dissociation and density functional theory calculations. Experiments on a triple quadrupole mass spectrometer showed that protonated myrcene undergoes two major unimolecular reactions losing propene and isobutene, and two minor reactions of ethene and propane loss. In each case, the product ion consists of a substituted five-member ring. Protonation of myrcene was found to form four distinct protomers, three of which can be significantly populated in the ion source. The observed fragmentation reactions were calculated and found to depend on the starting protomer. Each pathway consisted of several hydrogen-migration and ring-forming/opening steps on the way to the observed products. Likewise, protonation of linalool also produces three distinct protomers, with the global minimum being formed by protonation of a central double bond. The major reaction is water loss to form protonated myrcene, but two minor channels were also observed resulting in loss of acetone and isobutene. The calculated minimum energy reaction pathways were found to be consistent with the relative abundances of the ions in the experimental breakdown diagrams.

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 score0.014
Threshold uncertainty score0.507

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.218
Teacher spread0.214 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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