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Record W4412810733 · doi:10.1007/s10533-025-01254-3

Methane (CH4) oxidation in flooded forests of the amazon basin

2025· article· en· W4412810733 on OpenAlexaff
Pedro M. Barbosa, J. H. Amaral, John M. Mélack, Sally MacIntyre

Bibliographic record

VenueBiogeochemistry · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversité du Québec à Montréal
FundersInstituto Chico Mendes de Conservação da BiodiversidadeWashington Space Grant ConsortiumDivision of Environmental BiologyNational Aeronautics and Space AdministrationInstituto Nacional de Pesquisas da AmazôniaNational Science Foundation
KeywordsAmazon basinAnaerobic oxidation of methaneAmazon rainforestMethaneStructural basinEnvironmental scienceEcosystemHydrology (agriculture)GeologyEarth scienceGeomorphologyEcology

Abstract

fetched live from OpenAlex

Abstract Methane oxidation has been observed in a wide range of aquatic environments worldwide, and measurements are rare in tropical floodplains. The Amazon floodplain is one of the largest tropical wetlands with seasonally flooded forests representing up to 80% of the area of aquatic habitats in the lowland Amazon. Hence, we measured methane oxidation rates (Mox) in two different flooded forests ( várzea , in white waters; igapó , in black waters) and evaluated effects of dissolved oxygen and CH 4 concentrations, and water temperature on methane oxidation. We found high Mox in near-bottom waters associated with high CH 4 concentrations (1.0–2.4 µM) and hypoxia, with volumetric rates ranging from 9.8 to 73 mg C m −3 d −1 in the igapó , and from 2.3 to 101.4 mg C m −3 d −1 in the várzea . Depth integrated Mox rates ranged from 177 to 213 mg C m −2 d −1 for the igapó , and 159 mg C m −2 d −1 in the várzea , and were one to two orders of magnitude higher than CH 4 fluxes from water to the atmosphere, emphasizing the important role of Mox in attenuating CH 4 emissions from tropical flooded forests. The present study contributes to understanding of the complex processes involved in carbon dynamics on tropical floodplains.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.198
Teacher spread0.195 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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