Modeling the carbon cycle in Lake Matano
Bibliographic record
Abstract
Lake Matano, Indonesia, is a stratified anoxic lake with iron-rich waters that has been used as an analogue for the Archean and early Proterozoic oceans. Past studies of Lake Matano report large amounts of methane production, with as much as 80% of primary production degraded via methanogenesis. Low δ(13)C values of DIC in the lake are difficult to reconcile with this notion, as fractionation during methanogenesis produces isotopically heavy CO2. To help reconcile these observations, we develop a box model of the carbon cycle in ferruginous Lake Matano, Indonesia, that satisfies the constraints of CH4 and DIC isotopic profiles, sediment composition, and alkalinity. We estimate methane fluxes smaller than originally proposed, with about 9% of organic carbon export to the deep waters degraded via methanogenesis. In addition, despite the abundance of Fe within the waters, anoxic ferric iron respiration of organic matter degrades <3% of organic carbon export, leaving methanogenesis as the largest contributor to anaerobic organic matter remineralization, while indicating a relatively minor role for iron as an electron acceptor. As the majority of carbon exported is buried in the sediments, we suggest that the role of methane in the Archean and early Proterozoic oceans is less significant than presumed in other studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".