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Record W2507956124 · doi:10.4236/ti.2016.73011

Quantitative and Qualitative Study of Phenolic Compounds Involved in Germination Inhibition of Wheat under Water Deficit

2016· article· en· W2507956124 on OpenAlexvenueno aff
Abdelghani Chakhchar, Salama Aissam, Cherkaoui El Modafar

Bibliographic record

VenueTechnology and Investment · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsnot available
FundersUniversité Cadi Ayyad
KeywordsGerminationSalicylic acidPolyphenolFerulic acidChemistryPhenolic acidCultivarHorticultureWater stressPEG ratioBiologyFood scienceBiochemistry

Abstract

fetched live from OpenAlex

In the aim to study the contribution of phenolic compounds in the inhibition of seeds germination of wheat (Triticum aestivum L.) under water deficit, we have forwarded quantitatively and qualitatively the change of the phenolic compounds levels in the seeds of two varieties (Achtar and Tigre) submitted to water stress induced by PEG-6000 during the germination stage. The results revealed significant differences between the stress intensity and duration and the polyphenol content. Some germination traits were significantly correlated with water stress and polyphenols in the both varieties studied. The polyphenol content in Tigre was much higher than those noted in Achtar. Analysis of phenolic extracts by HPLC showed the dominance of hydroxycinnamic derivatives (97.2% and 94.5% in Tigre and Achtar, respectively). Regarding phenolic acid, the ferulic acid was the most dominant, and seemed to be related mainly to the inhibition of germination. Quantitatively, the Achtar variety was characterized by the presence of acid sinapic which was absent in Tigre variety. Furthermore, the water deficit appeared to stimulate the induction of salicylic acid accumulation in both varieties.

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 score0.513
Threshold uncertainty score0.086

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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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