Anaerobic methane oxidation in an East African great lake (Lake Kivu)
Bibliographic record
Abstract
Abstract. This study investigates methane (CH4) oxidation in the water column of Lake Kivu, a deep meromictic tropical lake containing large quantities of CH4 in the anoxic deep waters. Depth profiles of dissolved gases (CH4 and nitrous oxide (N2O)) and of the different potential electron acceptors for anaerobic methane oxidation (AOM) (nitrate, sulfate, iron and manganese) were determined during six field campaigns between June 2011 and August 2014. Bacterial abundance all along the vertical profiles was also determined by flow cytometry during three field campaigns, and denitrification measurements based on stable isotopes were performed twice. Incubation experiments were performed to quantify CH4 oxidation and nitrate consumption rates, with a focus on AOM, without and with an inhibitor of sulfate-reducing bacteria activity (molybdate). Nitrate consumption rates were measured in these incubations. Substantial CH4 oxidation activity was observed in oxic and anoxic waters, and in the upper anoxic waters of Lake Kivu, CH4 is a major electron donor to sustain anaerobic metabolic processes coupled to AOM. The maximum aerobic and anaerobic CH4 oxidation rates were estimated to 27 ± 2 and 16 ± 8 µmol L−1 d−1, respectively. We observed a decrease of AOM rates when molybdate was added for half of the measurements, strongly suggesting the occurrence of AOM linked to sulfate reduction, but an increase of AOM rates was observed for the other half. Nitrate reduction rates and dissolved manganese production rates tended to be higher with the addition of molybdate, but the maximum rates of 0.6 ± 0.02 and 11 ± 2 µmol L−1 d−1, respectively, were never high enough to explain AOM rates observed at the same depths. We also put in evidence a difference in relative importance of aerobic and anaerobic CH4 oxidation between the seasons, with a higher importance of aerobic oxidation when the oxygenated layer was thicker (in dry season).
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 | 0.001 |
| Science and technology studies | 0.001 | 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".