Temperature‐mediated microbial carbon utilization in China's lakes
Bibliographic record
Abstract
Microbes play an important role in aquatic carbon cycling but we have a limited understanding of their functional responses to changes in temperature across large geographic areas. Here, we explored how microbial communities utilized different carbon substrates and the underlying ecological mechanisms along a space-for-time substitution temperature gradient of future climate change. The gradient included 47 lakes from five major lake regions in China spanning a difference of nearly 15°C in mean annual temperatures (MAT). Our results indicated that lakes from warmer regions generally had lower values of variables related to carbon concentrations and greater carbon utilization than those from colder regions. The greater utilization of carbon substrates under higher temperatures could be attributed to changes in bacterial community composition, with a greater abundance of Cyanobacteria and Actinobacteriota and less Proteobacteria in warmer lake regions. We also found that the core species in microbial networks changed with increasing temperature, from Hydrogenophaga and Rhodobacteraceae, which inhibited the utilization of amino acids and carbohydrates, to the CL500-29-marine-group, which promoted the utilization of all almost carbon substrates. Overall, our findings suggest that temperature can mediate aquatic carbon utilization by changing the interactions between bacteria and individual carbon substrates, and the discovery of core species that affect carbon utilization provides insight into potential carbon sequestration within inland water bodies under future climate warming.
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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 | 0.000 | 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".