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Flooded Pantanal Forests Absorb CO2, While Grass-Dominated Savannas are Sources for the Atmosphere in South-Central Brazil

2024· article· en· W4410123362 on OpenAlexaff
Izaura De Oliveira Roberto, Higo J. Dalmagro, Paulo Henrique Zanella de Arruda, Ana Cristina Kubo Almada, Jonathan Willian Zangeski Novais, Kaio Henrique Hintz Soares Albano, Amanda Alves Rocha, Osvaldo Borges Pinto, George L. Vourlitis, Mark Stephen Johnson, Eduardo Guimarães Couto

Bibliographic record

VenueEnsaios e Ciência C Biológicas Agrárias e da Saúde · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicGeography and Environmental Studies
Canadian institutionsUniversity of British Columbia
FundersFundação Nacional de Desenvolvimento do Ensino Superior ParticularUniversidade Federal de Mato Grosso do SulCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsAtmosphere (unit)GeographyEnvironmental scienceAgroforestryMeteorology

Abstract

fetched live from OpenAlex

The Cerrado and the Pantanal are important ecosystems in the central region of Brazil. Cerrado vegetation has varying physiognomies that differ in height, cover and tree density, ranging from thick forests to pastures without woody vegetation. The Pantanal landscape consists of a mosaic of floodplain grasslands, open forests and temporary or permanent aquatic habitats. The present work aims to evaluate the energy balance and carbon (C) flows in an area of Cerrado (Campo Sujo) in the Baixada Cuiabana and forest areas in the Pantanal. Carbon flows in both areas were measured from micrometeorological towers equipped with eddy covariance sensors to measure energy and CO2 flows. The annual net flow of carbon (NEE) depended on the availability of water in both areas, where, depending on the climate regime, the ecosystem behaved as a net emitter of C to the atmosphere or a sink for atmospheric CO2. The Campo Sujo Cerrado became an emitter of NEE to the atmosphere (314 g C m-2) during the study years, especially under dry soil conditions, while in the Pantanal, NEE emissions increased during the dry season and absorption increased during the rainy season. The mean annual NEE in the Pantanal biome was -262 g C m-2, which proved to be a strong NEE sink. Therefore, it should be noted that the change in vegetation cover, mainly in the Pantanal, can have a significant impact on the NEE balance. Keywords: Cerrado. Pantanal. NEE. Carbon Flow. Eddy Covariance. Resumo Na região central do Brasil, encontram-se dois dos mais importantes ecossistemas brasileiros: o Cerrado e o Pantanal. A vegetação do Cerrado tem uma fisionomia específica, variando em altura, cobertura e densidade de árvores, desde matas cerradas até pastagens sem vegetação lenhosa. A paisagem do Pantanal consiste em um mosaico de pastagens inundáveis, florestas abertas e habitats aquáticos temporários ou permanentes. O presente trabalho tem como objetivo avaliar o balanço de energia e os fluxos de carbono em uma área do Cerrado de Campo Sujo na baixada cuiabana e áreas de floresta no Pantanal. Nas áreas de estudo, foram instaladas torres micrometeorológicas equipadas com sensores para medir os fluxos de energia e CO2. Em ambos os biomas, o fluxo líquido anual de carbono (NEE) depende da disponibilidade de água no sistema, onde, dependendo do regime de precipitação, o ecossistema pode se comportar como emissor ou sumidouro de NEE. O bioma Cerrado de Campo Sujo tornou-se um potencial emissor de NEE para a atmosfera (314 g C m-2 ano-1) durante os anos de estudo, especialmente sob condições de solo seco. No bioma Pantanal, as emissões de NEE aumentaram durante a estação seca e a absorção cresceu durante a estação chuvosa. O NEE médio anual no bioma Pantanal foi de -262 g C m-2, o que provou ser um forte sumidouro de NEE. Diante disso, observa-se que a mudança na cobertura vegetal, principalmente no Pantanal, pode ter um impacto significativo no balanço do NEE. Palavras-chave: Cerrado. Pantanal. NEE. Fluxo de Carbono. Eddy-Covariance.

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 score0.094
Threshold uncertainty score0.187

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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".

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Citations0
Published2024
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