Variability and controls of stable carbon isotopic fractionation during aerobic methane oxidation in temperate lakes
Bibliographic record
Abstract
The aerobic oxidation of methane (CH 4 ) by methanotrophic bacteria (MOB) is the major sink of this highly potent greenhouse gas in freshwater environments. Yet, CH 4 oxidation is one of the largest uncertain components in predicting the current and future CH 4 emissions from these systems. While stable carbon isotopic mass balance is a powerful approach to estimate the extent of CH 4 oxidation in situ , its applicability is constrained by the need of a reliable isotopic fractionation factor (α ox ), which depicts the slower reaction of the heavier stable isotope ( 13 C) during CH 4 oxidation. Here we explored the natural variability and the controls of α ox across the water column of six temperate lakes using experimental incubation of unamended water samples at different temperatures. We found a large variability of α ox (1.004–1.038) with a systematic increase from the surface to the deep layers of lake water columns. Moreover, α ox was strongly positively coupled to the abundance of MOB in the γ-proteobacteria class (γ-MOB), which in turn correlated to the concentrations of oxygen and CH 4 , and to the rates of CH 4 oxidation. To enable the applicability in future isotopic mass balance studies, we further developed a general model to predict α ox using routinely measured limnological variables. By applying this model to δ 13 C-CH 4 profiles obtained from the study lakes, we show that using a constant α ox value in isotopic mass balances can largely misrepresent and undermine patterns of the extent of CH 4 oxidation in lakes. Our α ox model thus contributes towards more reliable estimations of stable carbon isotope-based quantification of CH 4 oxidation and may help to elucidate large scale patterns and drivers of the oxidation-driven mitigation of CH 4 emission from lakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".