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Record W2970956570 · doi:10.5539/jas.v11n15p65

Physical Quality Indicators of an Oxisol Under Grass in the Agreste Region of Paraiba, Brazil

2019· article· en· W2970956570 on OpenAlexvenueno aff
Pedro Luan Ferreira da Silva, Flávio Pereira de Oliveira, Camila Costa da Nóbrega, Adriana Ferraz Martins, Walter Esfrain Pereira, Igor Botelho, Danillo Dutra Tavares, Tales Emanuel Duarte Santos

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsBrachiariaOxisolBulk densityAgronomyField capacityMathematicsEnvironmental scienceAnimal scienceSoil waterForageSoil scienceBiology

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the influence of different grass cultivars on physical attributes of an Oxisol in the Agreste region of Paraíba. The experiment was set up in 2005 in experimental areas of the Center of Agricultural Sciences of the Federal University of Paraíba, Areia-PB. The experimental design adopted was that of randomized complete blocks (RCB) with 5 treatments and 4 replications, in experimental plots of 50 m2. The treatments were the following: I. Brachiaria decumbens Stapf., II. Brachiaria brizantha, III. Brachiaria humidicola (Rendle) Schweickvr., IV. Brachiaria brizantha cv. MG5-Vitória and V. Brachiaria ruziziensis. In October 2018 samples of soil with disturbed and undisturbed structure were collected at the center of each experimental plot in the depth of 0.0-0.10 m for the determination of the following variables: soil porosity (total, macro and micro), bulk density, compaction degree, saturated hydraulic conductivity, field capacity, permanent wilting point, available water content, soil aeration capacity, clay dispersed in water, flocculation degree and aggregate stability index. The analysis of variance was performed and the means were compared by Tukey’s tests, principal component analysis and Pearson’s correlation analysis (p < 0.05). It is concluded that after the 13-year period, Brachiaria brizantha promoted improvements to the field capacity of the Oxisol. The other attributes were not physically altered. Main component analysis showed that the correlation values were more significant for the Brachiaria brizantha component. Pearson’s correlation was significant between field capacity and soil aeration capacity.

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)0.000
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 score0.762
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.279
Teacher spread0.256 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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