Biennial Mowing Maintains the Biomass and Functional Diversity of Semi-Arid Grassland
Bibliographic record
Abstract
Understanding the functional diversity relationship between biomass and plants is a key issue in biodiversity–ecosystem functionality and the utility of grassland. We conducted a five-year mowing experiment to examine the effects of the mowing frequency on biomass, plant species, and functional diversity of a natural plant community in a semiarid region of Inner Mongolia. A secondary objective was to test whether unmowed refuge areas within plots would mitigate the disturbance effect of mowing. The result showed that mowing disturbance reduced biomass by the greatest amount with mowing once every year (M1) and the least with M2. The biomass composition of M2 consisted of a greater mass of perennial species than in the other mowing treatments but was equivalent to the control (CK). However, mowing disturbances increased the plant species richness and M2 had the largest number of species. The community-weighted means (CWM) indices indicated that M2 produced the least detrimental effects on the grassland. Retaining unmowed refuge areas appeared to be ineffective in promoting beneficial community traits under M1 mowing regimes. Based on our results, we recommend that the semi-arid grassland be mowed every other year in order to optimize sustainable production.
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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".