The effects of weed competition, clipping and fertilization treatments on the productivity of cultivated meadows on the Qinghai-Tibetan Plateau
Bibliographic record
Abstract
The meadow ecosystem of the Qinghai-Tibetan plateau, the largest rangeland in China, has been degenerating recently from heavy grazing and soil erosion, resulting in decreased carrying capacity and canopy coverage. A field experiment was designed to test the effects of four treatments (density, fertilization, clipping, and species). The results show that a mixture of forage species provides the greatest increase meadow productivity and community stability. There was a 28% increase in target species yield in a two species mixture compared with a monoculture and a 103% increase in a three species mixture. Fertilization resulted in a 63% increase in the target species yield and a 54% increase in weed yield, but decreased weed abundance. Clipping had an adverse effect on meadow productivity and weed growth (abundance and yield), decreasing the target species yield by 46% relative to no clipping, and decreasing the weed yield by 6%. Elymus nutans was a competitive winner in all of the mixtures, regardless of treatment. A three-way ANOVA showed that the three species-mixture was the optimal combination for the development of a cultivated meadow. Clipping had no significant effect on the meadow yield but significantly decreased weed abundance. This three-species mixture not only increased yield, but also resistance to weed growth, thus making the mixture a good choice to improve rangeland, and provide benefits for both local economic development and environmental protection. Key words: Clipping, fertilization, mixture, rangeland, weed
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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 | 0.000 |
| 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) | 0.001 | 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".