Responses of plant community coverage to simulated warming and nitrogen addition in a desert steppe in Northern China
Bibliographic record
Abstract
Abstract Grassland communities occupy about 25 % of global land area and global warming could alter grassland plant community coverage. A field study was conducted to investigate the impact of soil warming (surface soil temperature increased by 1.3 °C) and nitrogen addition (100 kg N ha −1 year −1 ) on a desert steppe community in Inner Mongolia, China, from 2006 to 2011. Although there were strong intra‐ and inter‐annual variations, in general, warming had little effect on coverage of the four major species (two cool season C 3 species: Convolvulus ammannii and Stipa breviflora , and two warm season C 4 species: Cleistogenes songorica and Kochia prostrata ) over six growing seasons. The C 3 /C 4 coverage ratio consistently decreased with warming, with the decrease being statistically significant for one‐third of sampling dates. Warming caused a reduction (from 27.6 to 25.1 %) in peak season total plant community coverage. N addition did not affect the four main species, four functional groups (perennial grass, perennial forb, shrub and annual‐biennial) or the total plant community over the 6 years, except when precipitation was high (231 mm in 2008). The small reduction in plant community coverage demonstrated the resilience of desert steppe to warming. There could be a shift of C 3 to C 4 species because of warming, as indicated by the decrease in C 3 /C 4 ratio, but confirmation of this trend requires further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".