Improved Management of Grassland to Promote Sustainable Use Based on Farm Size
Bibliographic record
Abstract
Grassland farms form the basis of grassland resource management in China. Farm sizes in China are generally small, which obviously increases the risk of grassland ecosystems. It is necessary to analyze the impact of farm size on grasslands from the perspective of livestock production in order to improve grassland management. This study combines field investigations and statistical analysis from 2004 to 2020, using a total of 126 farms from the Xilinguole League of Inner Mongolia in China as samples. These sample farms are divided into large farms and small farms. Different production scale and management behaviors are explored, along with their different impacts on grassland resources use. The results show that the expansion of farm size is constrained by the government management policies. Different behaviors are adopted by large and small farms in terms of finance, grassland circulation, and overgrazing management. The differentiation mechanisms of different farm size and the utilization of grassland resources are clarified in this study. This work suggests that managers promote sustainable use based on farm size and build appropriate policies to avoid future risks. The results of this study can provide a framework for solving similar problems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".