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Record W2805833409 · doi:10.5539/jas.v10n7p85

Biological Fertilization as an Attenuation of Salinity Water on Beetroot (Beta vulgaris)

2018· article· en· W2805833409 on OpenAlexvenueno aff
Ednardo Gabriel de Sousa, Toshik Iarley da Silva, Thiago Jardelino Dias, Danrlei Varela Ribeiro, Álvaro Carlos Gonçalves Neto, Leonardo Vieira de Sousa, Anderson Carlos de Melo Gonçalves, Joana Gomes de Moura, José Sebastião de Melo Filho

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsBiofertilizerSalinityIrrigationFertilizerAgronomyEnvironmental scienceSaline waterAridHuman fertilizationSoil salinityChemistryBiologyEcology

Abstract

fetched live from OpenAlex

Salinity is one of the major obstacles of modern agriculture, especially in the semi-arid regions, since these have high rates of evaporation and water sources with high salt terrors. Thus, the present study aimed to investigate the attenuating effects of bovine biofertilizer and biological fertilizer under irrigation with saline waters on the morphological behavior of beetroots (Beta vulgaris L.). The design was randomized blocks in a factorial scheme 4 × 2 + 1, referring to the electrical conductivity of the irrigation water (ECw: 0.5, 1.5, 3.0 and 6.0 dS m-1) and application of bovine biofertilizer in the absence (BIO I), and presence of Microgeo® (BIO II) and a control (without fertilization and ECw 0.5 dS m-1). No effects of the factors evaluated on the gas exchange of beetroots were observed. However, the increase of ECw has negative effects on phytomass and growth of this crop, as the application of bio fertilizer favors some soil chemical characteristics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.036
GPT teacher head0.263
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2018
Admission routes1
Has abstractyes

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