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Record W4407732959 · doi:10.1139/cjfr-2024-0258

Silicon and salicylic acid mitigate water stress in <i>Cedrela fissilis</i> Vell. seedlings under water restriction

2025· article· en· W4407732959 on OpenAlexvenueno aff
Jéssica Aline Linné, Vanda Maria de Aquino Figueiredo, Wállas Matos Cerqueira, João Lucas da Costa Santos de Almeida, Antonio Augusto Souza Silva, Maílson Vieira Jesus, Cleberton Correia Santos, Silvana de Paula Quintão Scalon, Silvia Corrêa Santos

Bibliographic record

VenueCanadian Journal of Forest Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
FundersFundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do SulConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsWater stressSalicylic acidDrought stressBotanyChemistryBiology

Abstract

fetched live from OpenAlex

Supplementation with silicon (Si) and salicylic acid (SA) represents a mitigating solution to water deficit stress in some species. However, studies related to native tree species are scarce. This work evaluated the application of Si and SA doses in Cedrela fissilis Vell. seedlings during and after water restriction (WR). We had 8 treatments: control; WR and treatments with WR + 3 doses of Si (0.42, 0.84, and 1.68 g·L −1 ) and SA (100, 200, and 300 mg·L −1 ). Seedlings were evaluated at two periods: P0, when the photosynthetic rate ( A) of seedlings presented values close to zero; and REC, the period in which the previously stressed seedlings reached an A value equal to or greater than control. We observed that seedlings showed reduction in the photochemical and biochemical metabolism of photosynthesis. Foliar application of Si at 0.84 g·L −1 ensures metabolic adjustments in water use efficiency.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.993

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 science0.0000.000
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 head0.020
GPT teacher head0.265
Teacher spread0.245 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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