An empirical model to predict methane production in inland water sediment from particular organic matter supply and reactivity
Bibliographic record
Abstract
Abstract The highest CH4 production rates can be found in anoxic inland water surface sediments however no model quantifies CH4 production following fresh particular organic matter (POM) deposition on anoxic sediments. This limits our capability of modeling CH4 emissions from inland waters to the atmosphere. To generate such a model, we quantified how the POM supply rate and POM reactivity control CH4 production in anoxic surface sediment, by amending sediment at different frequencies with different quantities of aquatic and terrestrial POM. From the modeled CH4 production, we derived parameters related to the kinetics and the extent of CH4 production. We show that the extent of CH4 production can be well predicted by the quality (i.e., C/N ratio) and the quantity of POM supplied to an anoxic sediment. In particular, within the range of sedimentation rates that can be found in aquatic systems, we show that CH4 production increases linearly with the quantity of phytoplankton‐derived and terrestrially derived POM. A high frequency of POM addition, which is a common situation in natural systems, resulted in higher peaks in CH4 production rates. This suggests that relationships derived from earlier incubation experiments that added POM only once, may result in underestimation of sediment CH4 production. Our results quantitatively couple CH4 production in anoxic surface sediment to POM sedimentation flux, and are therefore useful for the further development of mechanistic models of inland water CH4 emission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".