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Record W4293793104 · doi:10.24980/ucm.v11i13.4211

MAPEAMENTO DO USO E OCUPAÇÃO DO SOLO DA ÁREA RURAL DO MUNICÍPIO DE SANTA ALBERTINA – SP: LEVANTAMENTO TÉCNICO

2022· article· pt· W4293793104 on OpenAlexaboutno aff
Lucas Estevam BIANCHO, Kaique Augusto Poltronieri Donatoni, Jaqueline Bonfim de Carvalho, Camila Fernandes Ferreira APARECIDO

Bibliographic record

VenueUnifunec Científica Multidisciplinar · 2022
Typearticle
Languagept
FieldEnvironmental Science
TopicRural Development and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsGeoprocessingGeographyAgricultural scienceForestryEnvironmental scienceCartography

Abstract

fetched live from OpenAlex

O uso e ocupação do solo nos permite identificar, planejar e prever eventos naturais e antrópicos como qualidade e quantidade de área, aptidão agrícola da região, culturas implantadas em expansão e perspectivas de mercado. O levantamento desse uso é fundamental para uma boa gestão de seus recursos, possibilitando maior renda, melhor tomada de decisão e uso mais sustentável. O objetivo do trabalho foi realizar o levantamento das mudanças da ocupação da área rural do município de Santa Albertina-SP, verificando os dados histórico de imagens de satélites do município. Os dados levantados foram do uso da ocupação do solo agrícola, conservação das áreas de preservação permanente e reserva legal. A pesquisa usou imagens de satélite SIRGAS 2000 do Google Earth Pro e para o geoprocessamento o software QGis 2.18 La Palmas. Os resultados da pesquisa apresentaram aumento de 61% na área de cana-de-açúcar do ano de 2010 para 2020 e uma redução de 19% na área de pastagem do ano de 2010 para 2020, as áreas de mata se mantiveram e setores como citros 84% e banana 88% de redução do ano 2010 para o ano de 2020 e em seringueira, ecoturismo, região urbana e região industrial houve um aumento (21%, 20% e 64%, respectivamente) do ano 2010 para o ano 2020. Conclui-se que, de acordo com o estudo do mapeamento, pode-se avaliar o desenvolvimento econômico das áreas e diagnosticar as culturas e atividades que estão em expansão e redução territorial. MAPPING OF THE USE AND USE OF THE SOIL IN THE RURAL AREA OF THE MUNICIPALITY OF SANTA ALBERTA–SP: TECHNICAL SURVEY ABSTRACT The soil use and occupation allows us to identify, plan and predict natural and anthropic events such as area quality and quantity, agricultural aptitude of the region, growing crops and market perspectives. The inventory of this use is fundamental for a good management of its resources, enabling higher income, better decision making, and a more sustainable use. The aim of the paper was to carry out a survey of the changes in the rural area occupation in the municipality of Santa Albertina-SP, comparing the historical data from satellite images of the municipality. The data collected were on the use and occupation of agricultural land, conservation of permanent preservation areas, and legal reserves. The research used SIRGAS 2000 satellite images from Google Earth Pro and for geoprocessing the QGis 2.18 La Palmas software. The findings of the survey showed a 61% increase in the sugarcane area from the year 2010 to 2020 and a 19% reduction in the pasture area from the year 2010 to 2020, the forest areas were maintained and sectors like citrus 84% and banana 88% reduction from the year 2010 to the year 2020 and in rubber trees, ecotourism, urban region and industrial region there was an increase (21%, 20% and 64%, respectively) from the year 2010 to the year 2020. It is concluded that, according to the mapping study, we may evaluate the economic development of the areas and diagnose the crops and activities that are expanding and reducing territorially. Keywords: Agricultural expansion. Georeferencing. Satellite images. Topographic survey.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0030.008
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0280.003

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 head0.013
GPT teacher head0.247
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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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