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Genotypic and environmental variations in phenolic acid and avenanthramide content of Canadian oat (Avena sativa)

2022· article· en· W4224303875 on OpenAlexafffundabout
Lovemore Nkhata Malunga, Nancy Ames, J. Mitchell Fetch, Thomas Netticadan, Sijo Joseph Thandapilly

Bibliographic record

VenueFood Chemistry · 2022
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsBrandon UniversityAgriculture and Agri-Food CanadaUniversity of Manitoba
FundersAgriculture and Agri-Food Canada
KeywordsAvenaCultivarRandomized block designGenotypeField experimentChemistryHorticultureAgronomyBiologyFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Phenolic compounds (PC) in oat may possess health promoting properties. This study evaluated the effect of genotype, environment, and their interaction on the phenolic acid and avenanthramide (AVN) concentration in Canadian oat. Six cultivars were grown at eight locations across Canada in a randomised complete block design with three field replicates. Free PCs were extracted from oat groat flour and analysed using a UPLC-PDA system. The cumulative concentration of free PCs and AVN ranged from 58 to 350 and 9 to 244 µg/g, respectively. The effect of environment was significant (p < 0.0001) for the concentration of all PCs. Cultivar differences significantly influenced the concentration of all PCs but AVNs A and B. The growing location explained > 68% of the variation in the concentration of AVN. Thus understanding the genotypic and environmental triggers of individual PCs may help agronomists and breeders strategize in selecting and growing oat cultivars of interest.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.594
Threshold uncertainty score0.808

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.197
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2022
Admission routes3
Has abstractyes

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