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Record W4366548264 · doi:10.5376/mpb.2023.14.0008

Effects of High Temperature and Waterlogging Stress on Cellulase Activity of Non-heading Chinese Cabbage

2023· article· en· W4366548264 on OpenAlexvenueno aff
Hongfang Zhu, Lu Gao, He Xiaoyan, Xiaofeng Li, Dandan Xi, Yuying Zhu

Bibliographic record

VenueMolecular Plant Breeding · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
Fundersnot available
KeywordsWaterlogging (archaeology)CellulaseBiologyEnzyme assayHorticultureEnzymeChemistryAgronomyEcologyBiochemistry

Abstract

fetched live from OpenAlex

Two non-heading Chinese cabbage varieties 'Heiyoudong' and 'Suzhouqing' were used as materials to study the effects of high temperature and waterlogging stress on the activities of enzymes related to cellulose synthesis in non-heading Chinese cabbage. The two varieties had different resistance to high temperature and waterlogging. Three temperature gradients of 24℃, 35℃ and 40℃ were set, and each temperature was set two treatments, waterlogging and non-waterlogging. The results showed that under high temperature stress, the CesA, SS, and KOR enzyme activities of 'Heiyoudong' and 'Suzhouqing' showed a trend of first increasing and then decreasing, while the trend of CE enzyme activity was opposite. Under the combined stress of high temperature and waterlogging, the changed trend of the enzyme activities related to cellulose synthesis in 'Heiyoudong' and 'Suzhouqing' is similar to that of a single high temperature stress, but the degree of impact is greater than that of a single high temperature stress. In addition, regardless of high temperature stress, high temperature and waterlogging combined stress, the increase in CesA, SS, and KOR activities in 'Heiyoudong' was greater than that of 'Suzhouqing', but the decrease was less than that of 'Suzhouqing'; The increase in CE activity of 'Suzhouqing' is less than that of 'Suzhouqing', but the decrease rate is greater than that of 'Suzhouqing'.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.304

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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
Published2023
Admission routes1
Has abstractyes

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