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Record W2979052652 · doi:10.5539/jas.v11n17p235

Nitrogen Fertilization in the Initial Growth of Khaya senegalensis A. Juss Plants Under Greenhouse Conditions

2019· article· en· W2979052652 on OpenAlexvenueno aff
Matheus da Silva Araújo, José Eduardo Dias Calixto Júnior, Vitor Corrêa de Mattos Barretto, Adilson Pelá, Rodrigo Tenório de Vasconcelos, Ednaldo Cândido Rocha

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsKhayaGreenhouseHuman fertilizationDry matterNitrogenCompletely randomized designBiomass (ecology)NutrientSowingBiologyHorticultureAgronomyDry weightMeliaceaeBotanyChemistryEcology

Abstract

fetched live from OpenAlex

African mahogany is an exotic specie and its cultivation has increased in Brazil due to the high value of its timber on the international market. Nutrition with nitrogen is an important factor for species with high biomass production and specific studies on this species are essential. The present study aimed to assess the initial growth of African mahogany plants submitted to nitrogen fertilization. The experiment was set up and carried out in a greenhouse, with 7 dm3 plastic pots using a oxisoil sampled from the surface layer. A completely randomized experimental design was used with five treatments and six replications. The treatments consisted of five N levels: 0, 40, 80, 120 and 160 mg dm-3, using urea as the source. The following were assessed at 180 days: height, stem diameter, leaf, stem, root and total dry matter, N content in the leaves in the African mahogany leaves. The African mahogany seedlings had high N demand and responded positively to the N used and the growth variables was positive with increase in N level. However, as it presented increasing linear effect, the N level can not be estimated, that would provide the maximum initial development for this plant species.

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 score0.657
Threshold uncertainty score0.119

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 science0.0010.000
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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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