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Record W3025294221 · doi:10.5539/jas.v12n6p189

Growth and Initial Development of Papaya Plants (Sunrise Solo) in Different Concentrations of Biostimulants

2020· article· en· W3025294221 on OpenAlexvenueno aff
Bruno Anderson Araújo Barros, Sammy Sidney Rocha Matías, Thamyres Yara Lima Evangelista, Mariane Sirqueira Nogueira, Thaís Paula Martins Nunes, Gasparino Batista de Sousa, Gustavo Alves Pereira

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth Enhancement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCaricaSowingCompletely randomized designHorticultureBiologyAgronomyBotany

Abstract

fetched live from OpenAlex

The objective of this research was to evaluate the vegetative growth of papaya seedlings, propagated by seeds, regarding the use and application rates of two biostimulants in two types of soil. The experiment was carried out at the State University of Piauí (UESPI)/Campus de Corrente, with papaya (Carica papaya L.) as a research culture, on a screen at 50% brightness. The completely randomized design consisted of four treatments arranged according to the following application doses (0, 4, 8, 12, and 16 ml) using the biostimulant Solofull® and Stimulate® via soil, with six replicates per treatment, totaling 24 experimental units. The soil used came from two situations, soil 1 (area in process of degradation, Gilbués—PI) and soil 2 (pasture area, Corrente, PI). At 65 days after sowing, height, stem diameter, number of true leaves, leaf area, height ratio of plants, and stem diameter and root length were evaluated. The data were submitted to analysis of variance. The degraded area soil provided the best growth of the aerial part. The types of biostimulants and application doses used in this study did not provide significant differences between treatments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.248
Teacher spread0.216 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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