Patterns and Regulation of Hypolimnetic CO<sub>2</sub> and CH<sub>4</sub> in a Tropical Reservoir Using a Process‐Based Modeling Approach
Bibliographic record
Abstract
Abstract Hypolimnetic waters and sediments of lakes and reservoirs are active sites of carbon dioxide (CO 2 ) and methane (CH 4 ) production and accumulation. These potent carbon greenhouse gases (C‐GHG) can be emitted to the atmosphere during water column mixing and, for hydropower reservoirs, through deep water discharge from the outflow. Deep CO 2 and CH 4 concentrations are regulated by sediment‐water flux, aerobic respiration, methanogenesis, CH 4 oxidation, and vertical diffusion. These processes are interlinked but have mostly been studied individually, making their relative importance and interactions difficult to disentangle. In this study, we combined empirical observations with an integrative process‐based modeling to examine ecosystem‐scale dynamics of CO 2 and CH 4 in the deep layer of a permanently stratified reservoir located on the tropical Borneo Island (Malaysia). Oxygen (O 2 ) was a key modulator of deep CO 2 and CH 4 metabolism, and shaped their spatial and temporal patterns within the reservoir. Our model reproduced the shape of the vertical profiles of gas concentration and isotopic signature, and suggested that deep C‐GHG stocks reached a steady‐state about 10 years after impoundment. Vertical gas diffusivity was the most determinant parameter controlling reservoir deep C‐GHG accumulation through its direct effect on gas transport and its interaction with water column metabolism through O 2 regulation. Model results highlighted the interactions, relative importance, and uncertainties of processes controlling deep C‐GHG fluxes. This case study offers an in‐depth comprehension of C‐GHG storage and emissions in a tropical system, as well as a mechanistic framework transferable to other lakes and reservoirs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".