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Record W2955736892 · doi:10.5539/jas.v11n11p245

The Influence of H2O2 Application Methods on Melon Plants Submitted to Saline Stress

2019· article· en· W2955736892 on OpenAlexvenueno aff
Adriana Silva Santos, Juliana Formiga Almeida, Márcio Santos da Silva, Jackson Silva Nóbrega, Thaís Batista de Queiroga, Jessica Alves Ribeiro Pereira, Jéssica Aline Linné, Fernando Antônio Lima Gomes

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsSalinityTransplantingHydrogen peroxideShootMelonAbiotic componentIrrigationSowingSaline waterHorticultureDry matterDry weightChemistrySalineAgronomyBotanyBiology

Abstract

fetched live from OpenAlex

The salinity in irrigation water is one of the most important causes to decline cultivated plants yield. The H2O2 application has shown efficiency as a stimulator and activator for antioxidative defense system in plants submitted to biotic and abiotic stresses. The objective of this study was to evaluate methods for hydrogen peroxide application as a strategy to minimize the effects of saline stress on melon plants. The experiment was designed in complete randomized blocks and set in 2 × 4 factorial scheme, consisting two levels for irrigation water salinity (S1 = 0.3 and S2 = 2.0 dS m-1) and four methods for hydrogen peroxide application (15 mM), (T1 = no peroxide application, T2= imbibition of seeds, T3 = at sowing, T4 = Foliar spraying), with five repetitions. It was evaluated the following variables at 58 days after transplanting: plant height, stem diameter, number of leaves, number of flowers, shoot dry mass, root dry mass and total dry mass. The results showed that salinity affected the growth, biomass accumulation and plant quality severely, with the highest losses promoted by the electrical conductivity of 2.0 dS m-1.

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)0.001
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 score0.880
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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