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Record W1992150144 · doi:10.5539/sar.v3n3p1

Influence of Drought and Wounding Stress on Soluble Phenols and Proteins in Potato Tubers

2014· article· en· W1992150144 on OpenAlexvenueno aff
Christina Wegener, Gisela Jansen, Hans‐Ulrich Jürgens

Bibliographic record

VenueSustainable Agriculture Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhenolsCultivarChemistryDrought stressHorticultureFood scienceBotanyBiologyBiochemistry

Abstract

fetched live from OpenAlex

<p>Potato is a valuable source for phytochemicals like vitamins, minerals, plant phenols, amino acids and proteins. However, environmental stress may affect the level of all these components. In this study, two purple breeding clones and one yellow fleshed cultivar (cv.) were assessed in their response to drought and wounding stress in two consecutive years. The plants were grown in the greenhouse under control (sufficient water supply) and with drought stress conditions. After harvest the tubers were analyzed for the content of soluble phenols and proteins in fresh tissue and after wounding. In addition, free amino acids (AAS) and activity of lipid acyl hydrolases (LAH) were assayed in control and drought stressed tubers. The results revealed significant differences in soluble phenols and proteins between genotypes, and that drought stress significantly increased the level of soluble proteins (P < 0.0001) and LAH activity (P < 0.001), but had no significant effect on the concentration of phenols. Moreover, total amounts of free AAS were higher in the drought stress variant. Wounding stress caused a significant increase of soluble phenols in cv. Agave. But, this was less prominent in purple clones which in general had higher contents of phenols. Proteins were also enhanced as a result of wounding, although, the effect of wounding stress on their level was smaller than that of drought stress.<strong></strong></p>

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)0.002
metaresearch head score (Gemma)0.001
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 score0.696
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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