Supplementary original data
Bibliographic record
Abstract
The ongoing climate change is predicted to induce more weather extremes such as frequent drought and high-intensity precipitation events, causing more severe drying-rewetting cycles in soil. However, it remains largely unknown how these changes will affect soil nitrogen (N)-cycling microbes and the emissions of potent greenhouse gas nitrous oxide (N2O). Utilizing a field precipitation manipulation in a semi-arid grassland on the Loess Plateau, we examined how precipitation reduction (ca. −30%) influenced soil N2O and carbon dioxide (CO2) emissions in field, and in a complementary lab-incubation with simulated drying-rewetting cycles. Results obtained showed that precipitation reduction stimulated plant root turnover and N-cycling processes, enhancing soil N2O and CO2 emissions in field, particularly after each rainfall event. Also, high-resolution isotopic analyses revealed that field soil N2O emissions primarily originated from nitrification process. The incubation experiment further showed that in field soils under precipitation reduction, drying-rewetting stimulated N mineralization and ammonia-oxidizing bacteria in favor of genera Nitrosospira and Nitrosovibrio, increasing nitrification and N2O emissions. These findings suggest that moderate precipitation reduction, accompanied with changes in drying-rewetting cycles under future precipitation scenarios, may enhance N cycling processes and soil N2O emissions in semi-arid ecosystems, feeding positively back to the ongoing 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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.742 | 0.553 |
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".