Reconstructing the seasonal dynamics and relative contribution of the major processes sustaining CO<sub>2</sub> emissions in northern lakes
Bibliographic record
Abstract
Abstract Lake CO2 emissions are an important component of the carbon balance of northern landscapes, yet the temporal dynamics of the underlying mechanisms sustaining CO2 emissions are less understood. Here, we reconstruct the major biotic and abiotic processes influencing CO2 dynamics over an annual cycle in three limnologically different lakes, using a combination of empirical measurements and process‐based modeling. Our results suggest that the relative importance of each process sustaining CO2 emissions is not only variable among lakes, but also highly variable among seasons within one lake. Spring CO2 emissions were largely sustained by the release of under ice accumulation (between about 50–100%), although photo‐chemical DOC mineralization and hydrologic CO2 loading were also relatively important. In summer, due to warmer temperature, pelagic and benthic metabolism were the main sources of CO2 emissions. In the fall, lake CO2 emissions were generally sustained by hydrologic CO2 inputs, while hypolimnetic CO2 accumulation and release also contributed to fall CO2 emission in the deepest lake. On an annual basis, lake CO2 emissions ranged between 21.4 g C m−2 yr−1 and 55.5 g C m−2 yr−1. Our results confirm that the major processes all contributed significantly to CO2 emissions, but their relative contributions were modulated by the seasonal patterns in climate and hydrology, and by differences in morphology and organic carbon inputs among lakes. These lake‐ and season‐specific features need to be considered both in the upscaling of lake processes at regional scales, and in predicting lake CO2 emissions under scenarios of climate and environmental 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 | 0.000 |
| 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".