MétaCan
Menu
Back to cohort
Record W2896532993 · doi:10.5539/jas.v10n11p190

Soil Attributes Using Different Agricultural Use Systems in the Rio Grande do Norte Semiarid, Brazil

2018· article· en· W2896532993 on OpenAlexvenueno aff
Ana Karenina Fernandes de Sousa Ribeiro, Jeane Cruz Portela, Rafael Oliveira Batista, Carolina Malala Martins Souza, Joseane Dunga da Costa, Phâmella Kalliny Pereira Farias, Tarcísio José de Oliveira Filho

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsAlkalinityAtterberg limitsLithologySoil waterSalinitySoil scienceEnvironmental scienceTotal organic carbonHydrology (agriculture)Environmental chemistryGeologyChemistryGeotechnical engineeringGeochemistry

Abstract

fetched live from OpenAlex

The semiarid region is extremely fragile to anthropogenic actions. Thus, the objective of this study is to evaluate the physical and chemical attributes of soils with different agricultural uses. The research was carried out in the municipality of Governador Dix-Sept Rosado. Fertility and physical analyses were performed. The results were interpreted by multivariate analysis. The soils that presented a eutrophic character were influenced by lithology. In the Cambissolo (Haplustepts), there was an increase in the limits of liquidity and plasticity due to the increase of the clay fraction and total organic carbon. By the particle size analysis, the profiles presented variations in textural classes. We concluded that the physical attributes moisture, liquidity limit, plasticity limit, clay plasticity index, thin sand and the chemical attributes pH, (H + Al), V and PST were the most sensitive for the distinction of environments. The studied areas presented acidity reactions to alkalinity with presence of Al3+, (H + Al) and high salinity.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.035
GPT teacher head0.242
Teacher spread0.207 · 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.

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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicSoil Management and Crop YieldFrench-language works237,207