Rates and pathways of sedimentary organic matter mineralization in two basins of a boreal lake: Emphasis on methanogenesis and methanotrophy
Bibliographic record
Abstract
Abstract Sediment porewater was analyzed at several sampling dates in two adjacent basins of an oligotrophic boreal lake, one basin perennially oxygenated (Basin A) and the other occasionally anoxic (Basin B). Depth concentration profiles of methane (CH 4 ), dissolved inorganic carbon (DIC), and electron acceptors were modeled with a one‐dimensional transport‐reaction equation to constrain the depth intervals (zones) where solutes are produced/consumed in the top 10 cm of the sediment column, and to obtain the net reaction rates in each zone. This multicomponent geochemical modeling reveals that CH 4 was produced below 4–7 cm depth at lower rates in Basin A (250–800 fmol cm −2 s −1 ) than in Basin B (1900–6500 fmol cm −2 s −1 ) and that methanogenesis accounted for 30–64% and 84–100% of the sediment organic matter (OM) mineralization in Basins A and B, respectively. We show that methanogenesis did not always yield equimolar amount of CH 4 and DIC, as would be expected from the fermentation of the model molecule CH 2 O. While ∼50% of the CH 4 produced in Basin A is oxidized in the sediment column, this proportion decreases to ∼20% in Basin B. Dioxygen is by far the main electron acceptor for CH 4 and OM oxidations in both basins. Methanotrophy in the sediment, however, is not limited to the ∼4‐mm thick surface layer in which O 2 diffuses from bottom water but occurs down to 4–7 cm depth where O 2 is transported through bioirrigation. Thermodynamic calculations suggest that, in addition to O 2 , Fe oxyhydroxides, and sulfate may serve as oxidants for methanotrophy in that zone. We predict that Basin B sediments release more CH 4 than DIC whereas Basin A sediments mainly export DIC. This study highlights that small changes in hypolimnetic O 2 levels may significantly alter the magnitude of OM mineralization pathways and the fate of CH 4 in boreal lake sediments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".