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Antioxidant response mechanisms and thermotolerance thresholds in cotton (Gossypium hirsutum L.) under progressive heat stress

2024· article· W7142544056 on OpenAlexaboutno aff
Hayley Chen, Marcus Leblanc, Priya Sharma

Bibliographic record

VenueInternational Journal of Research in Agronomy · 2024
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicResearch in Cotton Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsAntioxidantMalondialdehydeProlinePhotosynthesisOxidative stressGossypium hirsutumHeat stressCatalase

Abstract

fetched live from OpenAlex

Rising global temperatures are pushing cotton production into increasingly hostile thermal environments, yet the biochemical boundaries of cotton thermotolerance remain poorly mapped. This research characterized antioxidant enzyme responses and oxidative damage indicators in cotton (cv. Deltapine 1646) seedlings exposed to progressive heat stress (30, 38, 42, and 46 °C) for 72 hours in controlled environment chambers at the University of Saskatchewan during 2022. Antioxidant enzyme activities (SOD, CAT, APX, GR, POD) increased significantly at 38 and 42 °C but declined sharply at 46 °C, revealing a thermotolerance threshold between 42 and 46 °C. Malondialdehyde content rose 2.4-fold at 42 °C and 4.8-fold at 46 °C relative to controls. Proline accumulation peaked at 42 °C. Leaf photosynthetic rate declined linearly with temperature, falling 67% at 46 °C. These findings define a biochemical tipping point where cotton antioxidant defenses become overwhelmed, with direct implications for breeding programs targeting heat-resilient 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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

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 head0.068
GPT teacher head0.394
Teacher spread0.325 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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