Quantifying emissions of methane derived from anaerobic organic matter respiration and natural gas extraction in Lake Erie
Bibliographic record
Abstract
Abstract Despite a growing awareness of the importance of inland waters in regional and global carbon (C) cycles, particularly as sources of the greenhouse gases carbon dioxide (CO 2 ) and methane (CH 4 ), very little is known about C sources and fluxes in the Laurentian Great Lakes, Earth's largest surface freshwater system. Here, we present a study of CH 4 dynamics in Lake Erie, which has large spring algae blooms linked to fertilizer runoff and followed by hypoxia, as well as an extensive network of natural gas wells and pipelines in Canadian waters. Lake Erie is a positive source of CH 4 to the atmosphere in late summer, even in shallow regions without water column hypoxia. Stable isotopic measurements indicate that both biogenic and thermogenic CH 4 contribute to emissions from Lake Erie. We estimate that Lake Erie emits 1.3 ± 0.6 × 10 5 kg CH 4 ‐C d −1 in late summer, with approximately 30% of CH 4 derived from natural gas infrastructure. Additional work is needed to determine the spatial and temporal dynamics of CH 4 emissions from Lake Erie and to confirm estimates of source contribution. Studies of the C cycle in large lakes are not as straightforward as those in smaller lakes, as, in addition to O 2 availability, subsurface currents and high winds may exert significant control over dissolved CH 4 patterns. If climate warming and increasing precipitation intensity lead to increased algal biomass and/or greater extent and duration of hypoxia, this may increase emissions of CH 4 from Lake Erie in a positive feedback to climate change.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".