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Record W7005078344

Polyamines-mediated regulation of enzymatic antioxidative response to excess soil moisture in soybean (Glycine max L.)

2017· dissertation· en· W7005078344 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2017
Typedissertation
Languageen
FieldMedicine
TopicBiological and pharmacological studies of plants
Canadian institutionsnot available
Fundersnot available
KeywordsSeedlingAntioxidantMoistureAbiotic stressReactive oxygen speciesEnzymeWater contentAbiotic component
DOInot available

Abstract

fetched live from OpenAlex

Excess soil moisture creates an oxygen deficient growth environment that adversely affects plant growth and has led to greater agricultural losses than any other abiotic stress factor affecting the Canadian prairies, especially Manitoba. The hypoxic environment limits cellular respiration in root tissues and disrupts the antioxidative systems of plants, resulting in accumulation of reactive oxygen species which react with and damage essential cellular components. The antioxidative capacity of plants and therefore their tolerance to stress conditions such as excess moisture can be improved by treatments with growth regulators such as polyamines, which play important roles in mediating plant response to their environment. This thesis investigated the role of treatments with polyamines in improving seedling growth and enzymatic antioxidant capacity in the tissues of seedlings and young vegetative soybean plants under excess soil moisture conditions. The results indicate that treatment with polyamines can improve the growth of excess moisture-stressed seedlings and this is associated with an enhanced antioxidative response with improved expression of antioxidative genes and activity of the corresponding enzymes. Furthermore, the results of this study indicate that polyamine treatments also increase the enzymatic antioxidant capacity in the leaf tissues of young soybean plants exposed to excess moisture.

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)0.000
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 score0.759
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.025
GPT teacher head0.266
Teacher spread0.241 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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