The response of grassland productivity, soil carbon content and soil respiration rates to different grazing regimes in a desert steppe in northern China
Bibliographic record
Abstract
Soil respiration is a major process for organic carbon losses from arid ecosystems. A field experiment was conducted in 2010 and 2012 on the responses to continuous grazing, rotational grazing and no grazing on desert steppe vegetation in northern China. The growing season in 2010 was relatively dry and in 2012 was relatively wet. The results showed that mean soil respiration was the highest with no grazing in both growing seasons. Compared with no grazing, the soil respiration was decreased by 23.0% under continuous grazing and 14.1% under seasonal rotational grazing. Soil respiration increased linearly with increasing soil water gravimetric content, aboveground net primary productivity (ANPP), belowground net primary productivity (BNPP) and soil carbon and nitrogen contents across the 2 years, whereas a negative correlation was detected between soil respiration and soil temperature. A significant decrease in soil respiration was observed under both continuous grazing and in seasonal rotational grazing in the dry growing season, but no significant difference was detected in the wet growing season. In the wet year, only a non-significant difference in soil respiration was observed between different grazing types. Patterns of seasonal precipitation strongly affected the temporal changes of soil respiration as well as its response to different grazing types. The findings highlight the importance of differences in abiotic (soil temperature, soil water gravimetric content and soil carbon and nitrogen contents) and biotic (ANPP, BNPP and litter mass) factors in mediating the responses of soil respiration to the different grazing regimes.
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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 | 0.000 |
| 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".