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Record W2847165212 · doi:10.5539/jas.v10n8p352

Malpighia emaginata D.C. Growth in Several Substrates and Salt Waters

2018· article· en· W2847165212 on OpenAlexvenueno aff
Jackson Silva Nóbrega, Ivando Comandante Macedo Silva, Israel Almeida da Silva, Reginaldo Gomes Nobre, Francisco Romário Andrade Figueiredo, Francisco Marto de Souza, Reynaldo Teodoro de Fátima, Jean Télvio Andrade Ferreira, Rodrigo Garcia Silva Nascimento

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsSalinityManureRandomized block designBiomass (ecology)IrrigationHorticultureDry weightAgronomySoil salinityGerminationChemistryBiologyEcology

Abstract

fetched live from OpenAlex

The acerola tree is one of the most promising fruit trees of the Brazilian fruit sector, demanding the development of studies that indicate the proper conditions to improve its production under adverse conditions. Consequently, our purpose was to evaluate the growth of Malpighia emaginata D.C in several substrates and under ascending levels of irrigation water salinity. The experiment was conducted in a 4 × 5 factorial scheme randomized block design, which comprised four substrates (S1 = soil; S2 = soil with a 10% addition of cattle manure; S3 = soil with a 10% addition of organic compounds; and S4 - soil with a 5% addition of cattle manure and a 5% addition of organic compounds) and five CEa salt levels (0.3, 1.0, 1.7, 2.7, and 3.5 dS m-1). The plant height and stem diameter variables were not affected by the studied factors. The interaction between salinity and the substrates affected the root growth and the build-up of the plants’ fresh and dry biomass, resulting in the increase of the values obtained by the substrates containing manure and organic compounds. The seedlings’ quality, represented by the height/stem diameter and aerial part/root dry mass ratios, and by the Dickson quality index, indicated that the plants produced in the substrates 2 and 3 were more vigorous.

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)0.000
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 score0.513
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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