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Record W4408430395 · doi:10.5194/egusphere-egu25-14261

Bridging the Gap: Integrating Top-Down and Bottom-Up Measurement Approaches to close the Amazon CH4 emissions budget 

2025· preprint· en· W4408430395 on OpenAlexaff
Akshay Nataraj, Frédéric Despagne, Julio Lobo Neto, Doug Baer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsABB (Canada)
Fundersnot available
KeywordsBridging (networking)Amazon rainforestEnvironmental scienceTop-down and bottom-up designPhysicsComputer scienceEcologyBiology

Abstract

fetched live from OpenAlex

This study is part of an international mission in the Amazon rainforest, involving researchers from the Federal University of Rio de Janeiro, Universities of Leeds, Linköping, British Columbia, and coordinated by Prof. Vincent Gauci and Dr. Sunitha Pangala. The primary objective is to reconcile top-down methane (CH₄) emission estimates, derived from remote sensing data over Amazon floodplains, with bottom-up measurements obtained from field studies. Previous satellite observations indicated a discrepancy of 20 million tons of CH₄ emitted annually, a significant gap that could not be fully explained by ground-based sources. This project aimed to resolve this difference by integrating both remote sensing satellites and field data using ABB’s Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) to understand and bring to light the different dynamics involved in methane emissions in the Amazon rainforest.Reconciling top-down and bottom-up carbon budgets can be particularly challenging in specific ecosystems where topography complicates site access and sampling. Under such conditions, the availability of compact and rugged cavity-enhanced laser-based analyzers offering sub-ppb precision is invaluable for environmental scientists. ABB’s portable greenhouse gas ultraportable analyser, GLA132-GGA (47 cm × 35.56 cm × 17.78 cm) is capable of monitoring CH4 with a 1 s precision of 1.4 ppb, which can be improved to 0.2 ppb with 100 s averaging time. The analyser is based on OA-ICOS technology that combines high precision capabilities through enhanced cavity path length and robustness to mechanical vibrations, which is crucial to field applications. Scientists used semi-rigid custom chambers wrapped around the trunk of floodplain trees and connected them to the GLA132-GGA to measure individual CH4 emissions from 2357 trees in 13 floodplain sites.The findings provide a crucial link to reconcile the 20-million-ton discrepancy in Amazon's methane budget. Scaled estimates of methane flux emitted from floodplain trees align closely with the missing methane observed in previous satellite data. During the rainy season, when Amazon tree roots become submerged, trees have evolved specialized adaptations to enhance oxygen supply to their roots by enlarging pores in their stems. This adaptation inadvertently facilitates the release of methane, produced by microorganisms in the waterlogged soil, through the same pore openings. Floodplain trees thus function as natural chimneys, venting substantial quantities of methane into the atmosphere. These large emissions from floodplain trees play a pivotal role in closing the Amazon methane budget.Furthermore, a second campaign revealed that methane produced deep within the soil column can also escape to the atmosphere via tree roots, even when the water table is below the surface. Regression analysis demonstrated that, while methane emissions show negligible response to increased flood levels above the soil surface, there is a clear dependence of whole-tree methane emissions on the presence of submerged roots. This highlights the importance of floodplain trees in regulating methane fluxes across varying hydrological conditions, underscoring their significant role in the global methane cycle.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0010.001
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 head0.062
GPT teacher head0.241
Teacher spread0.179 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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