MétaCan
Menu
Back to cohort
Record W2885095816 · doi:10.5539/jas.v10n9p105

Soil Water Retention in the Semiarid Region of Brazil

2018· article· en· W2885095816 on OpenAlexvenueno aff
A.C. Silva, Jeane Cruz Portela, Rafael Oliveira Batista, Rutilene Rodrigues da Cunha, Joaquim Emanuel Fernandes Gondim, Luiz Eduardo Vieira de Arruda, José Francismar de Medeiros

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsSiltPermanent wilting pointSoil waterSoil scienceSoil morphologySoil testField capacityEnvironmental sciencePedotransfer functionLoamGranulometrySoil horizonSoil classificationGeologyHydraulic conductivityGeomorphology

Abstract

fetched live from OpenAlex

From the physics point of view, soil structure is a dynamic attribute that is affected by genetic conditions and anthropogenic changes and requires an integrated approach. Soil water retention curve is one of the main tools used in soil structure evaluations. The objective of this work was to evaluate the structural and chemical attributes of soils of different classes and agroecosystems in the Terra de Esperança Settlement (Governador Dix Sept Rosado, Rio Grande do Norte, Brazil) to distinguish these environments. Disturbed and undisturbed soil samples were collected in horizons of 10 soil profiles of the soil classes: Cambissolo Háplico (Haplustepts), Latossolo Vermelho-Amarelo (Eutrustox), Chernossolo Rêndzico (Calciustolls), and Neossolo Flúvico (Usticfluvents). The soil physical attributes evaluated were granulometry, soil density, total porosity, aeration porosity, macroporosity, microporosity, field capacity, permanent wilting point, available water, and water retention curve. The results were expressed in averages of four replicates per horizon (in laboratory) by multivariate analysis, which detected the most sensitive attributes for the distinction of the environments. The soil physical attributes of the different classes and its inorganic fractions, especially silt and clay, were determinant to distinguish the environments; they affected the microporosity; aeration porosity; and available water. The source material of the Chernossolo Rêndzico, which is rich in calcium and magnesium, affected its physical attributes, characterized by the predominance of the silt fraction. Clay was the determinant fraction of the Cambissolo Háplico, and Neossolo Flúvico; and the sand fraction on the surface layer, and clay fraction in the Bw horizon were determinant of the Latossolo Vermelho-Amarelo. The more expressive physical attributes were soil density, sand content, macroporosity (Latossolo Vermelho-Amarelo), microporosity, field capacity, available water, permanent wilting point, total organic carbon, mass-based moisture, volume-based moisture, clay, aeration porosity (Cambissolo Háplico, and Neossolo Flúvico), and silt (Chernossolo Rêndzico).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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 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

Explore more

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