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Record W7052731349

Soil functioning in a toposequence under rainforest in São Paulo, Brazil Funcionamento do solo em uma topossequência sob Mata Atlântica em São Paulo, Brasil

2013· article· en· W7052731349 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectrical and Electromagnetic Research
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterRainforestEntisolOxisolBulk densityTransectSoil morphologySoil classificationHydrology (agriculture)
DOInot available

Abstract

fetched live from OpenAlex

Studies of soil-water dynamics using toposequences are essential to improve the understanding of soil-water-vegetation relationships. This study assessed the hydro-physical and morphological characteristics of soils of Atlantic Rainforest in the Parque Estadual de Carlos Botelho, state of São Paulo, Brazil. The study area of 10.24 ha (320 x 320 m) was covered by dense tropical rainforest (Atlantic Rainforest). Based on soil maps and topographic maps of the area, a representative transect of the soil in this plot was chosen and five soil trenches were opened to determine morphological properties. To evaluate the soil hydro-physical functioning, soil particle size distribution, bulk density (r), particle density (r s), soil water retention curves (SWRC), field saturated hydraulic conductivity (Ks), macroporosity (macro), and microporosity (micro) and total porosity (TP) were determined. Undisturbed samples were collected for micromorphometric image analysis, to determine pore size, shape, and connectivity. The soils in the study area were predominantly Inceptisols, and secondly Entisols and Epiaquic Haplustult. In the soil hydro-physical characterization of the selected transect, a change was observed in Ks between the surface and subsurface layers, from high/intermediate to intermediate/low permeability. This variation in soil-water dynamics was also observed in the SWRC, with higher water retention in the subsurface horizons. The soil hydro-physical behavior was influenced by the morphogenetic characteristics of the soils.<br>O estudo da dinâmica da água no solo utilizando topossequências é de grande importância para melhor compreender as relações solo-água-vegetação. Objetivou-se, com este trabalho, caracterizar físico, hídrica e morfologicamente os solos da mata do Parque Estadual de Carlos Botelho. A parcela abrange uma área de 10,24 ha (320 x 320 m), localizada sob Floresta Ombrófila Densa (Mata Atlântica de Encosta). Com base nos mapas de solos ultradetalhados e mapas planialtimétricos dessa área, escolheu-se uma transeção representativa dos solos da parcela; nessa, foram abertas cinco trincheiras, onde foi feita a descrição morfológica dos diferentes horizontes dos solos. No estudo da dinâmica da água no solo, foram feitas análises granulométricas, de densidade do solo e partículas, de curvas de retenção e de medidas de condutividade hidráulica saturada no campo, utilizando-se o permeâmetro de Guelph. Coletaram-se amostras indeformadas para realizar análises de imagens. Os Cambissolos apresentaram-se como os solos predominantes na parcela, mas também foram encontradas manchas de Neossolos e Gleissolos. No estudo da caracterização físico-hídrica dos solos da topossequência escolhida, observou-se mudança na condutividade hidráulica entre as camadas superficiais e subsuperficiais, de alta/intermediária para intermediária/baixa permeabilidade. Essa variação do comportamento da água no solo também pôde ser observada nas curvas de retenção de água, que evidenciou maior retenção de água nos horizontes subsuperficiais. Fatores como porosidade total, dimensão, forma e conexão entre os poros foram analisados por meio da análise de imagens. O comportamento físico-hídrico dos solos foi influenciado pelas características morfogênicas do solo.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.000

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 head0.112
GPT teacher head0.474
Teacher spread0.363 · 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
Published2013
Admission routes1
Has abstractyes

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