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Record W4402901682 · doi:10.1038/s41598-024-71114-x

Biodiversity in nutrients and biological activities of 14 highbush blueberry (Vaccinium corymbosum L.) cultivars

2024· article· en· W4402901682 on OpenAlexafffund
Sabina Lachowicz, Anubhav Pratap‐Singh, Ireneusz Ochmian, Ireneusz Kapusta, Agata Kotowska, S. Pluta

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVacciniumEricaceaeCultivarNutrientBiodiversityBiologyBotanyHorticultureEcology

Abstract

fetched live from OpenAlex

, pancreatic lipase inhibitory effect, α-amylase, and α-glucosidase, anti-bacterial) of 14 highbush blueberries (Vaccinium corymbosum L.) cultivars (Northern type) as well as a principal component analysis (PCA) to assess the variation of these properties in the context of biodiversity. Most of the cultivars in this research have been first presented in this paper. Phytochemical profiling of the tested highbush blueberry fruit revealed 75 bioactive compounds, including 5 macroelements, 7 microelements, 7 monophosphate nucleotides, 15 anthocyanins, 1 phenolic acid, 14 flavonols, 11 essential amino acids, 8 non-essential amino acids, 2 sugars, 7 organic acids. The PCA showed that the profile and contents of the analyzed compounds as well as their anti-bacterial, antioxidant, anti-diabetic, and anti-obesity potentials depended significantly on the tested cultivars. Thus, the study provides comprehensive data on cultivar-specific biodiversity and correlations that can be used to design novel extracts rich in polyphenolic, amino acids, and/or minerals extracts from the selected cultivars of highbush blueberry as natural and alternative sources to fulfill the growing industry demand for supplements, pharmaceuticals, and nutraceutical products.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.034
GPT teacher head0.250
Teacher spread0.216 · 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 designObservational
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

Citations18
Published2024
Admission routes2
Has abstractyes

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