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Record W2388176183

Effects of Salt Stress on 1-DNJ Content in Mulberry Leaf

2014· article· en· W2388176183 on OpenAlexaff
Ding Jun-na

Bibliographic record

VenueAnhui nongye kexue · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsScience North
Fundersnot available
KeywordsSalt (chemistry)HorticultureChemistryFood scienceBotanyBiology
DOInot available

Abstract

fetched live from OpenAlex

[Objective] To investigate effects of salt stress on 1-DNJ content in mulberry leaf.[Method] With Heilongjiang mulberry variety Qinglongsang as test material,different concentration salt stress treatments were conducted.HPLC method was used to determine 1-DNJ content.[Result] The results showed that the DNJ contests of mulberry is higher than CK under the salt stress,and 50 mmol/L NaCl mulberry leaf in solution with DNJ contents reach maximum 1.51 mg/g after 31 days.Under NaCl stress,1-DNJ content in upper leaves of mulberry is higher than that of the lower leaves.[Conclusion]The appropriate salt concentration can effectively improve 1-DNJ content in mulberry leaf.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.195
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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