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Record W4317617511 · doi:10.14295/bjs.v2i3.280

Lipid peroxidation within different amaranth cultivars

2023· article· en· W4317617511 on OpenAlexaff
Sylvestre Havugimana, Irina Kiseleva, Daniel Nsengumuremyi

Bibliographic record

VenueBrazilian Journal of Science · 2023
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsAmaranthLipid peroxidationMalondialdehydeCultivarAmaranthus hypochondriacusChemistryAmaranthus cruentusThiobarbituric acidFood scienceHorticultureBotanyBiologyAntioxidantBiochemistry

Abstract

fetched live from OpenAlex

In natural environments, plants are exposed to biotic and abiotic stresses during their whole life circle. Moreover, lipid peroxidation is a physiological indicator of the above stress responses, hence is often used as a biomarker to assess stress-induced cell damage or death. This study evaluated the lipid peroxidation of base and stress leaf discs for nine amaranth cultivars. The feasibility of optical density with λ = 532 and λ = 600 nm was investigated, and the malondialdehyde (MDA) concentration intensity was determined using the TBA method, especially the Thiobarbituric acid (TBA) extinction coefficient to detect its content. Furthermore, MDA values were ranging from 0.007 ± 0.001 mM/g-1 to 0.013 ± 0.002 mM/g-1 and from 0.016 ± 0.002 mM/g-1 to 0.035 ± 0.008 mM/g-1 for base and stress conditions respectively. This study represented high MDA content under water stress and low MDA content detection in leaves of A. caudatus L., A. hypochondriacus L., A. cruentus L., and A. hybridus L. cultivars. This indication defines the better antioxidant activity of these cultivars.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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