Large sediment methane production potential in reservoirs compared to lakes and rivers
Bibliographic record
Abstract
Abstract Inland waters emit a globally significant amount of methane (CH4) into the atmosphere. Measurements of potential CH4 production rates in the sediment can help constrain the magnitude of CH4 sources and time‐averaged emission rates. We explored the magnitude, variability, and drivers of potential CH4 production rates in the sediment, based on compiled measurements (238 sediment cores from 72 aquatic systems) following a standardized laboratory incubation procedure. The data reveal > 4‐fold higher potential CH4 production rates in reservoir sediments than lakes and > 14‐fold higher than rivers after being standardized for temperature. Sediment organic carbon content and depth below the sediment–water interface are universal drivers for potential CH4 production rates across freshwater ecosystems. The disproportional high CH4 production rate in sediments from human‐made water bodies calls for more comprehensive monitoring of their CH4 emissions to inform carbon footprint and inventory efforts. This first meta‐analysis of potential CH4 production rates in sediments from different types of freshwater aquatic systems may help with process‐based modeling of CH4 emissions from individual water bodies in larger‐scale assessments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".