Productivity responses of different functional groups to litter addition in typical grassland of Inner Mongolia
Bibliographic record
Abstract
Aims Much research has been done on litter in forest ecosystems,but little has been done in grassland ecosys-tems,although litter plays an important role in grasslands. Our objectives were to determine how litter affects aboveground biomass and productivity of different functional groups and whether litter addition has a positive or negative effect in typical grassland. Methods We added litter to typical grassland after frost in October 2002. Then we sampled peak standing crop in August from 2003 to 2007. We determined productivity by species by clipping the vegetation in two 20 cm × 50 cm quadrates. SAS 9.0 was used to analyze the data. Important findings Litter addition significantly increased aboveground biomass,especially in the first year after treatment; however,no significant differences (p 0.05) in productivity among litter addition treatments were found in the following years. Biomass was significantly different among years (p 0.001). Effects of litter addition on each functional group were not significant (p 0.05). PCA analysis of each functional group in different years showed that productivity depended on the competition and compensation effect between perennial bunch grasses and perennial forbs. With greater amounts of litter,the competition effect and the correlation of these two functional groups decreased.
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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) | 0.001 | 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".