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Record W3180809039 · doi:10.1590/0103-8478cr20200979

Seasonal variation in essential oil content and chemical profile of mint in southeast of Brazil

2021· article· en· W3180809039 on OpenAlexfundno aff
Joyce Pereira Alvarenga, Andreísa Flores Braga, Fernanda Ventorim Pacheco, Alexandre Alves de Carvalho, José Eduardo Brasil Pereira Pinto, Suzan Kelly Vilela Bertolucci

Bibliographic record

VenueCiência Rural · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of TorontoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSeasonalityMentholEssential oilChemical compositionMenthoneComposition (language)Spring (device)HorticultureBotanyBiologyChemistryEcology

Abstract

fetched live from OpenAlex

ABSTRACT: This study evaluated the influence of seasonality in essential oil (EO) chemical composition of Mentha x piperita L. and Mentha viridis L. grown in southeast of Brazil. Leaves were collected from November 2011 until August 2013, in the middle of the following seasons: Spring (November), Summer (February), Autumn (May) and Winter (August). EO extraction was carried out by hydrodistillation in Clevenger apparatus for 1 hour. Chemical composition of EO was analyzed by GC-FID and GC-MS. The EO content and its chemical composition were influenced by seasonality in both species. The highest EO content was obtained for M. x piperita during spring (4.26%) and for M. viridis during spring (3.30%) and summer (3.70%). Mentha x piperita increased menthol (16.31 to 41.26%), neomenthol (3.02 to 6.39%) and menthone (5.56 to 41.58%) contents during spring and summer, respectively. Whereas, M. viridis EO content did not show a quality response pattern in relation to seasonality. Therefore, harvest time for M. x piperita is recommended during spring, and for M. viridis is recommended during spring and summer.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.126

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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