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Record W2806374793 · doi:10.5539/jas.v10n7p455

Silicon Potential as Attenuator of Salinity Effects on Growth and Post-harvest Quality of Lettuce

2018· article· en· W2806374793 on OpenAlexvenueno aff
Hozano de Souza Lemos Neto, Marcelo de Almeida Guimarães, Rosilene Oliveira Mesquita, Ítalo Marlone Gomes Sampaio, Ana Régia Alves de Araújo Hendges, Alexandre Bosco de Oliveira

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsSalinityShootRandomized block designFactorial experimentHorticultureTitratable acidHalotoleranceChemistryAgronomyBiologyMathematicsEcology

Abstract

fetched live from OpenAlex

Silicon (Si) is a mineral element that provides many benefits to plants and may also assist in tolerance to abiotic stresses such as salinity. The aim of the study was to evaluate the use of Si as a possible attenuator of the deleterious effects of salinity on growth and post-harvest of lettuce. A completely randomized design with four replicates was performed in a 3 × 2 factorial, consisting of three salinity levels (1.65, 3.65, 7.65 dS m-1) and two levels of silicon (0.0 and 2.0 mM). Salinity reduced all variables related to plant growth. For shoot fresh and dry mass, it was observed that NaCl caused a reduction of 79.80 and 80%, respectively, in comparison to the control. However, parameters related to post-harvest such as titratable acidity, soluble solids and vitamin C increased with salinity. Although not effective in reducing saline stress on plant growth, Si provided improvements in the variables related to lettuce post-harvest. Salinity significantly reduced plant growth in the presence and absence of Si, but provided better post-harvest quality with Si.

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)0.002
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 score0.585
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.258
Teacher spread0.246 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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