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Record W4412494034 · doi:10.1002/cbdv.202501398

Characterization of the Volatile Compounds of the Hardwood Portion of <i>Betula papyrifera</i> Marshall From Quebec, Canada

2025· article· en· W4412494034 on OpenAlexafffundabout
David Fortier, Jean‐Christophe Séguin, Normand Voyer

Bibliographic record

VenueChemistry & Biodiversity · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNatural product bioactivities and synthesis
Canadian institutionsUniversité LavalCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesUniversité Laval
KeywordsHardwoodChemistrySolid-phase microextractionGas chromatography–mass spectrometryMass spectrometryGas chromatographyFood scienceOrganic chemistryChromatographyBotany

Abstract

fetched live from OpenAlex

Betula papyrifera Marshall (paper birch) hardwood is an abundant yet underutilized resource for Quebec's forestry industry. We investigated the volatile compounds of the hardwood extracted using hydrodistillation (HD) and headspace solid-phase microextraction (HS-SPME) and analyzed them by gas chromatography-mass spectrometry (GC-MS) and GC-flame ionization detection. HD produced an essential oil with a low average yield (0.010% ± 0.001%), from which we identified 51 compounds, dominated by linoleic acid and its oxidation products. HS-SPME provided a complementary profile, with 50 compounds identified, including aromatics and sesquiterpenes absent from the essential oil. The findings suggest that direct valorization of B. papyrifera hardwood for its volatile secondary metabolites is limited due to low yields and the prevalence of common compounds. Nevertheless, the study provides novel insights into the volatile chemical composition of B. papyrifera, contributing to the fundamental understanding of its extractives profile.

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 score0.088
Threshold uncertainty score0.978

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 head0.003
GPT teacher head0.159
Teacher spread0.156 · 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
Published2025
Admission routes3
Has abstractyes

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