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Silicon application for the production and quality of raspberry fruit in a subtropical region

2023· article· en· W4389299656 on OpenAlexaff
Alexandre Dias da Silva, Rafael Pio, Letícia Alves Carvalho Reis, Muhammad Siddique Afridi, Natália Ferreira Suárez, Pedro Maranha Peche, Carlos Henrique Milagres Ribeiro

Bibliographic record

VenuePesquisa Agropecuária Brasileira · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversity of Guelph
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBlowing a raspberryRubusTransplantingHorticultureSubtropicsHuman fertilizationCropRespirationBiologySeedlingAgronomyEnvironmental scienceBotanyEcology

Abstract

fetched live from OpenAlex

Abstract The objective of this work was to evaluate the effect of silicon (Si) on the cultivation and quality of raspberries (Rubus idaeus). The experiment consisted of seven treatments and four blocks located in a subtropical region. Each plot consisted of three pots with one seedling of 'Batum' raspberry. In each pot, the treatment consisted of Si doses at 0, 50, 100, 200, 400, 800, or 1600 mg dm-3, which were applied to the soil 15 days after the transplanting of the seedlings. Field analyses were performed by measuring chlorophyll a and b, water potential, and production. Fruit were analyzed for color, firmness, respiratory rate, soluble solids, and pH. Fertilization with Si stimulates the increase of fruit number and of the raspberry production per plant. The Si application increases the fruit production and fruit firmness; however, it reduces the water potential and respiration rate.

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)0.001
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 score0.735
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.056
GPT teacher head0.288
Teacher spread0.232 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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