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Record W2334101861 · doi:10.1139/cjpp-2012-0244

Why fruits are rich in antioxidants? An opinion review<sup>,</sup>

2012· review· en· W2334101861 on OpenAlexaffvenue
Ashok K. Grover

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2012
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGerminationRecalcitrant seedAntioxidantReactive oxygen speciesContext (archaeology)SeedlingBiologyOxidative damageBotanyEmbryoHorticultureCell biologyBiochemistry

Abstract

fetched live from OpenAlex

We rarely consider whether and how plants benefit from making antioxidant-rich fruits, despite our dependence on fruits as routine sources of these compounds. The hypothesis presented here is that storage of the antioxidant materials is advantageous to the survival of the plant species. This hypothesis is based on the premise that at different stages from flower bud opening to seedling formation, the concentrations of the reactive oxygen species (ROS) needed vary tremendously. Exposing seeds of several plant species to ROS aids germination. However, ROS can cause considerable damage by mutagenesis during plant embryogenesis. It is suggested that the antioxidant-rich environment in fruits protects the developing plant embryos from this damage. It also allows for an antioxidant environment for packaging the embryos into seeds with tight seed coats. After fruit maturation and seed dispersal, a prolonged exposure to oxygen and moisture enables the seeds to produce the ROS needed for seed germination. There is a simultaneous increase in the ROS scavenging systems to allow for protection of the dividing cells afterwards. These observations are unified into the hypothesis that the antioxidant rich fruits aid in the survival of plant species, and discussed in the context of vascular plant evolution.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.005

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 head0.070
GPT teacher head0.336
Teacher spread0.266 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2012
Admission routes2
Has abstractyes

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