Large‐scale patterns in summer diffusive <scp>CH</scp><sub>4</sub> fluxes across boreal lakes, and contribution to diffusive C emissions
Bibliographic record
Abstract
Lakes are a major component of boreal landscapes, and whereas lake CO2 emissions are recognized as a major component of regional C budgets, there is still much uncertainty associated to lake CH4 fluxes. Here, we present a large-scale study of the magnitude and regulation of boreal lake summer diffusive CH4 fluxes, and their contribution to total lake carbon (C) emissions, based on in situ measurements of concentration and fluxes of CH4 and CO2 in 224 lakes across a wide range of lake type and environmental gradients in Québec. The diffusive CH4 flux was highly variable (mean 11.6 ± 26.4 SD mg m(-2) d(-1) ), and it was positively correlated with temperature and lake nutrient status, and negatively correlated with lake area and colored dissolved organic matter (CDOM). The relationship between CH4 and CO2 concentrations fluxes was weak, suggesting major differences in their respective sources and/or regulation. For example, increasing water temperature leads to higher CH4 flux but does not significantly affect CO2 flux, whereas increasing CDOM concentration leads to higher CO2 flux but lower CH4 flux. CH4 contributed to 8 ± 23% to the total lake C emissions (CH4 + CO2 ), but 18 ± 25% to the total flux in terms of atmospheric warming potential, expressed as CO2 -equivalents. The incorporation of ebullition and plant-mediated CH4 fluxes would further increase the importance of lake CH4 . The average Q10 of CH4 flux was 3.7, once other covarying factors were accounted for, but this apparent Q10 varied with lake morphometry and was higher for shallow lakes. We conclude that global climate change and the resulting shifts in temperature will strongly influence lake CH4 fluxes across the boreal biome, but these climate effects may be altered by regional patterns in lake morphometry, nutrient status, and browning.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".