Large increases in emissions of methane and nitrous oxide from eutrophication in Lake Erie
Bibliographic record
Abstract
Abstract Eutrophication is linked to greenhouse gas emissions from inland waters. Phytoplankton blooms in Lake Erie, one of Earth’s largest lakes, have increased with nutrient runoff linked to climate warming, although greenhouse gas emissions from this or other large eutrophic lakes are not well characterized. We measured greenhouse gases around Lake Erie in all four seasons and found that CH 4 and N 2 O emissions have increased 10 times or more with re-eutrophication, especially during and after phytoplankton blooms. Lake Erie is a positive source of CH 4 throughout the entire year and around the entire lake, with the highest emissions in spring and summer near the mouth of the Maumee River. While Lake Erie is an overall N 2 O source, it is an N 2 O sink in winter throughout the lake and in some locations during large phytoplankton blooms. We estimate that Lake Erie emits ~6300 metric tons of CH 4 -C yr −1 (± 19%) and ~600 metric tons N 2 O-N yr −1 (± 37%): almost 500,000 metric tons CO 2 -eq yr −1 total. These results highlight the gravity of eutrophication-related increases in large lake GHG emissions: an overlooked, but potentially major feedback to global 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 | 0.000 | 0.001 |
| 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".