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Record W2366318466

Effect of Sand Priming on Aged Seed Vigor and Physiological Changes of Transgenic Insect Resistant Cotton

2013· article· en· W2366318466 on OpenAlexvenueno aff
Yurong Jiang

Bibliographic record

VenueSeed · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsSeedlingPriming (agriculture)Point of deliveryMalondialdehydeBiologyHorticultureSuperoxide dismutaseCultivarPeroxidaseAgronomyAntioxidantEnzymeBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Seeds of transgenic insect resistant cotton cultivars,CCRI 41and Z-905,together with their genetic background cultivars CCRI 23 as contrast,were used to investigate the effects of sand priming on changes of cotton seed vigor,field emergence and seedling growth characteristics after artificial aging treatment.The research showed that sand priming had a significant effect on artificial aged seed vigor and seedling growth of the three tested cotton cultivars.The results of biochemical analysis showed that the activities of superoxide dismutase(SOD) and peroxidase(POD) in cotyledons and roots of CCRI 41 and Z-905 cotton seedlings increased significantly,after they were treated by seed priming with sand,comparing with their corresponding control.CCRI 23 with priming treatment maintained higher SOD and POD activities in its roots than its unprimed control.Meanwhile,sand priming dramatically reduced malondialdehyde(MDA) accumulation in cotyledons of the three varieties.The experiment indicated that sand priming could enhance the aging tolerance of seedlings due to increasing the antioxidant enzyme activity and reducing the malondialdehyde(MDA) accumulating in the cotton seedlings,which suggested that sand priming may help to improve seedling establishment and enhance the ability of aging tolerance in cotton.The results showed that the transgenic insect resistant cotton Z-905 and CCRI 41 had a better priming effect than that of the contrast CCRI 23 which had a higher anti-aging ability.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.027
GPT teacher head0.240
Teacher spread0.212 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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