Assessing factors underlying variation of CO2 emissions in boreal lakes vs. reservoirs
Bibliographic record
Abstract
Reservoirs and lakes were compared to test the hypothesis that they are similar with respect to factors driving the variation in CO(2) emissions to the atmosphere. Understanding this variation is necessary for the assessment of the contribution of these freshwater ecosystems to the global carbon cycle. This study, in contrast to previous ones, included analyses of the relationships between CO(2) emissions and microbial communities. Pooled data (lakes and reservoirs) showed that variations in CO(2) emissions were strongly related to variations in temperature, dissolved organic matter (DOM) quality, and bacterial production (BP). Results also showed that lakes were characterized by higher water temperature, lower DOM quality, larger size of Daphnia, and enriched δ(13) C zooplankton compared to reservoirs. Moreover, interactions within plankton communities and relationships between CO(2) emissions and zooplankton δ(13) C signatures differed in lakes vs. reservoirs, indicating among-system type differences in food web structure and carbon cycling. As a result of these ecosystem-type characteristics, CO(2) emission variation was mainly explained by temperature and BP in lakes, and by DOM quality and the ratio of phytoplankton biomass to microheterotroph biomass in reservoirs. These results showed that differences in temperature and DOM quality between lakes and reservoirs translate into differences in microbial interactions and ultimately in the importance of factors driving CO(2) emissions to the atmosphere. They indicated that considering microbial communities and environmental variables such as temperature and DOM quality can help improve our understanding of the variation in CO(2) emissions from freshwater ecosystems.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".