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Record W2881858341 · doi:10.5539/jas.v10n8p328

Spatial Variability in Stability of Aggregates and Organic Matter of an Oxisol

2018· article· en· W2881858341 on OpenAlexvenueno aff
Ismênia Ribeiro de Oliveira, Jussara Silva Dantas, Letícia da Silva Ribeiro, James Ribeiro de Azevedo, Lauter Silva Souto, Francisco Alves da Silva, Diana Ferreira de Freitas, Sibele Caroline Pinheiro Amorim

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsOxisolLatosolOrganic matterTillageSoil scienceSoil organic matterSpatial variabilityEnvironmental scienceSoil waterMathematicsAgronomyChemistryStatistics

Abstract

fetched live from OpenAlex

Soil preparation may break its structure, destabilize the aggregates, and cause the loss of organic matter (OM). The study of spatial variability of soil attributes is an important indicator of soil physical quality. The aim of this study was to describe the spatial variability of the stability of aggregates and organic matter in Oxisol (Yellow Latosol) under different management systems.We collected simple samplings of soil in the eastern mesoregion of Maranhão, Brazil. Experimental areas with two distinct management systems were studied: conventional tillage and no-tillage. In each experimental area, we fitted a rectangular mesh of 50 points with 40m of spacing and 0.00 to 0.20 mof depth. The response variables were: weighted mean diameter (WMD); geometric mean diameter (GMD); percentage of aggregates (on classes of size between 1-2 mm and above 2 mm); and organic matter (OM). The no-tillage management showed high values of WMD, GMD, class of aggregates and OM. Maps of WMD and GMD were spatially correlated to OM map at no-tillage management. Soil properties had a spatial-dependent structure. The management system influenced the stability of aggregates and the amount of organic matter.

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 score0.503
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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