Low contribution of oxic methane production in shallow productive lakes
Bibliographic record
Abstract
Whereas the occurrence of oxic methane (CH₄) production (OMP) in the oxygenated water column of lakes is widely accepted, its mechanisms, isotopic signature, and contribution to total CH₄ emissions remain uncertain. Evidence suggests that phytoplankton produces CH4, but it is unclear how this pathway contributes to ecosystem OMP rates. Shallow lakes are often productive and feature high phytoplankton biomass, which could potentially lead to high OMP rates and a substantial contribution to CH4 emissions. Here we present results of a field mesocosm study carried out in three shallow lakes in the Pampean Plain (Argentina), designed to assess their ambient OMP dynamics. We combined this with laboratory experiments designed to estimate the potential CH4 production by phytoplankton strains from these systems. We demonstrate that OMP occurred in all lakes, albeit at low rates; all tested phytoplankton strains produced CH4, yet this production contributed up to 15% to OMP rates, implying that other pathways dominate the observed OMP. The contribution of OMP to lake CH4 diffusive emissions was low for all lakes and likely influenced by lake morphometry, suggesting that, despite their high phytoplankton abundances, other sources—such as sediment CH4 production and/or lateral inputs—dominate CH4 emissions in these ecosystems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".