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Sistema de informação geográfica como apoio ao levantamento detalhado de solos do Vale dos Vinhedos

2008· article· pt· W1972314702 on OpenAlexaff
Eliana Casco Sarmento, Carlos Alaberto Flores, Eliseu José Weber, Heinrich Hasenack, R. O. Potter

Bibliographic record

VenueRevista Brasileira de Ciência do Solo · 2008
Typearticle
Languagept
FieldEnvironmental Science
TopicSoil and Land Suitability Analysis
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsGeologyHumanitiesPhysicsMineralogyGeographyGeomorphologyArt

Abstract

fetched live from OpenAlex

O presente trabalho propõe o emprego de geotécnicas no apoio a um levantamento detalhado de solos, desde a coleta de dados em campo até à delimitação das unidades de solo e, posteriormente, a elaboração de um mapa final. A área de estudo corresponde a uma carta topográfica na escala 1:5.000 do Vale dos Vinhedos, na região da Serra Gaúcha, Rio Grande do Sul, Brasil. O material utilizado consiste em dados planialtimétricos de um levantamento aerofotogramétrico, receptores GPS (Global Posigioning System) e softwares de SIG (Sistemas de Informação Geográfica). Os softwares de SIG foram utilizados para integrar os dados de campo com as informações cartográficas e para analisar o relevo por meio de um MNT (Modelo Numérico do Terreno). Os resultados mostraram que o método permite a obtenção de um mapa digital georreferenciado, em que as unidades de mapeamento estão fortemente associadas com as fases de relevo, melhorando sua consistência e confiabilidade e facilitando seu uso futuro para outras aplicações.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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