Sources and processes sustaining surface CO <sub>2</sub> and CH <sub>4</sub> fluxes in a tropical reservoir: the importance of water column metabolism
Bibliographic record
Abstract
Abstract. Freshwaters are important emitters of carbon dioxide (CO2) and methane (CH4) to the atmosphere, two potent greenhouse gases (GHG). While aquatic surface GHG fluxes have been extensively measured, there is much less information about their underlying sources. In lakes and reservoirs, surface GHG can originate from horizontal riverine flow, the hypolimnion, littoral sediments, and water column metabolism. These processes are generally studied separately, leading to a fragmented assessment of their relative role in sustaining CO2 and CH4 surface fluxes. In this study, we quantified sources/sinks of CO2 and CH4 in the epilimnion along a hydrological continuum in a permanently stratified tropical reservoir (Borneo Island). Results showed that horizontal inputs are an important source of both CO2 and CH4 (18 to 100 % of surface emissions) in the upstream reservoir branches. However, this contribution fades along the hydrological continuum, becoming negligible in the main basin of the reservoir, where CO2 and CH4 are uncoupled and driven by different processes. In the main basin, vertical CO2 inputs and sediment CH4 inputs contributed to on average 60 and 23 % respectively to the surface fluxes of the corresponding gas. Water column metabolism exhibited wide amplitude and range for both gases, making it the most influential but uncertain component in the epilimnetic gas budgets. Overall our results show that while sources sustaining surface CO2 and CH4 fluxes vary spatially and between the two gases, internal water metabolism remains a dominant driver. However, this study also highlights challenges and knowledge gaps related to estimating ecosystem-scale CO2 and CH4 metabolism, which hinder aquatic GHG flux predictions.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".