eLife assessment: Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment
Bibliographic record
Abstract
Warming and precipitation anomalies affect terrestrial carbon balance partly through altering microbial eco-physiological processes (e.g., growth and death) in soil.However, little is known about how such processes responds to simultaneous regime shifts in temperature and precipitation.We used the 18 O-water quantitative stable isotope probing approach to estimate bacterial growth in alpine meadow soils of the Tibetan Plateau after a decade of warming and altered precipitation manipulation.Our results showed that the growth of major taxa was suppressed by the single and combined effects of temperature and precipitation, eliciting 40-90% of growth reduction of whole community.The antagonistic interactions of warming and altered precipitation on population growth were common (~70% taxa), represented by the weak antagonistic interactions of warming and drought, and the neutralizing effects of warming and wet.The members in Solirubrobacter and Pseudonocardia genera had high growth rates under changed climate regimes.These results are important to understand and predict the soil microbial dynamics in alpine meadow ecosystems suffering from multiple climate change factors. eLife assessmentThis important study addresses the long-term effect of warming and precipitation on microbial growth, as a proxy for understanding the impact of global warming.The evidence that warming and altered precipitation exhibit antagonistic effects on bacterial growth is compelling and advances our understanding of microbial dynamics affected by environmental factors.This study will interest microbial ecologists, microbiologists, and scientists generally concerned with 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".