Nexus of grazing management with plant and soil properties in northern China grasslands
Bibliographic record
Abstract
Grasslands provide habitats for living organisms and livelihoods for ~800 million people globally. Many grasslands in developing countries are severely degraded. Some measures have been taken to curb the trend of degradation for decades. It is important to determine how decade-long rejuvenation efforts affected grassland ecosystems. We identified 65 data-rich studies based on six criteria, from >2500 relevant publications, and generated a dataset with 997 rows and 12 variables. The dataset covers different grazing intensities (grazing exclusion, light, moderate, and heavy grazing) and their impacts on plant traits (vegetation coverage, aboveground and root biomass, and plant diversity) and soil physiochemical properties (bulk density, moisture content, organic C, total and available N, total and available P, C:N ratio, and pH). The dataset could be used to (i) quantify the effectiveness of rejuvenation processes by determining the impact on plant community and soil properties, (ii) perform comprehensive analyses to elucidate large-picture effects of grazing management and rejuvenation, and (iii) analyze the impact of grass-climate-soil-human interactions on grassland ecosystem sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".