The methane cycle in ferruginous Lake Matano
Bibliographic record
Abstract
In Lake Matano, Indonesia, the world's largest known ferruginous basin, more than 50% of authigenic organic matter is degraded through methanogenesis, despite high abundances of Fe (hydr)oxides in the lake sediments. Biogenic CH₄ accumulates to high concentrations (up to 1.4 mmol L⁻¹) in the anoxic bottom waters, which contain a total of 7.4 × 10⁵ tons of CH₄. Profiles of dissolved inorganic carbon (ΣCO₂) and carbon isotopes (δ¹³C) show that CH₄ is oxidized in the vicinity of the persistent pycnocline and that some of this CH₄ is likely oxidized anaerobically. The dearth of NO₃⁻ and SO₄²⁻ in Lake Matano waters suggests that anaerobic methane oxidation may be coupled to the reduction of Fe (and/or Mn) (hydr)oxides. Thermodynamic considerations reveal that CH₄ oxidation coupled to Fe(III) or Mn(III/IV) reduction would yield sufficient free energy to support microbial growth at the substrate levels present in Lake Matano. Flux calculations imply that Fe and Mn must be recycled several times directly within the water column to balance the upward flux of CH₄. 16S gene cloning identified methanogens in the anoxic water column, and these methanogens belong to groups capable of both acetoclastic and hydrogenotrophic methanogenesis. We find that methane is important in C cycling, even in this very Fe-rich environment. Such Fe-rich environments are rare on Earth today, but they are analogous to conditions in the ferruginous oceans thought to prevail during much of the Archean Eon. By analogy, methanogens and methanotrophs could have formed an important part of the Archean Ocean ecosystem.
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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 | 0.001 | 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) | 0.001 | 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".