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Record W4404682033 · doi:10.1002/pld3.70016

Leaf pigmentation in <scp><i>Cannabis sativa</i></scp>: Characterization of anthocyanin biosynthesis in colorful Cannabis varieties

2024· article· en· W4404682033 on OpenAlexafffund
Kristina Gagalova, Yifan Yan, Shumin Wang, Till Matzat, Simone D. Castellarin, İnanç Birol, David Edwards, Mathias Schuetz

Bibliographic record

VenuePlant Direct · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsUniversity of British ColumbiaKwantlen Polytechnic UniversityCanada's Michael Smith Genome Sciences Centre
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaMitacsCanada Foundation for Innovation
KeywordsCannabis sativaAnthocyaninCannabisBotanyBiosynthesisBiologyGeneMedicineBiochemistryPsychiatry

Abstract

fetched live from OpenAlex

Abstract Cannabis plants produce a spectrum of secondary metabolites, encompassing cannabinoids and more than 300 non‐cannabinoid compounds. Among these, anthocyanins have important functions in plants and also have well documented health benefits. Anthocyanins are largely responsible for the red/purple color phenotypes in plants. Although some well‐known Cannabis varieties display a wide range of red/purple pigmentation, the genetic underpinnings of anthocyanin biosynthesis have not been well characterized in Cannabis. This study unveils the genetic diversity of anthocyanin biosynthesis genes found in Cannabis, and we characterize the diversity of anthocyanins and related phenolics found in four differently pigmented Cannabis varieties. Our investigation revealed that the genes 4CL , CHS , F3H , F3′H , FLS , DFR , ANS , and OMT exhibited the strongest correlation with anthocyanin accumulation in Cannabis leaves. The results of this study enhance our understanding of the anthocyanin biosynthetic pathway and shed light on the molecular mechanisms governing Cannabis leaf pigmentation.

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 score0.785
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.235
Teacher spread0.218 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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