Carbon Storage and Sequestration Under Different Stocking Rates in a Eurasian Desert Steppe in China
Bibliographic record
Abstract
The research on carbon source/sink of terrestrial ecosystem is an important part of global climate change. Under the sustainable grazing management, the carbon storage in grassland ecosystem will increase, which promotes the carbon sequestration in the grassland area. In order to understand the carbon sequestration in grazing system of desert steppe, a sheep grazing experiment for completely random block design was conducted in desert steppe. There were 4 stocking rates and 3 replications in this experiment. The effects of annual precipitation and stocking rate on the carbon sequestration of desert steppe were compared. The stocking rate treatments were as follows: no grazing, light grazing, moderate grazing, and heavy grazing. Plant composition, biomass, individual plant carbon, soil carbon, emission of soil and livestock, and carbon flux of ecosystem were measured. We discussed carbon storage and carbon sequestration based on the above indicators. The results showed that stocking rate has different effects on the aboveground net primary production, belowground net primary production, carbon storage of grassland ecosystem, net ecosystem exchange and soil respiration. We further analyzed the impact factors of different variables, understood the carbon sequestration and transition process from plant to soil in this steppe. Finally, we concluded that the optimal stocking rate in desert steppe according to the vegetation, balance of soil nutrients and livestock performance, provides the theoretical reference for the sustainable grassland management based on grassland carbon sequestration.
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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".