Controlling factors of plant community composition with respect to the slope aspect gradient in the Qilian Mountains
Bibliographic record
Abstract
Abstract Slope aspect can affect soil temperature and soil type distribution, which, in turn, is likely to influence plant community composition. Three Qilian mountains, located in the northeastern part of the Qinghai–Tibetan Plateau, China, with four distinct slope aspects including south‐facing (SF), southwest‐facing (SW), northwest‐facing (NW), and north‐facing (NF) slope aspects, were studied to investigate the impact of slope aspect on plant assemblages. The results indicated that the environmental conditions were favorable under the NF and NW slope aspects as the soil water, soil organic carbon (SOC), and soil total nitrogen (STN) contents were significantly higher, and soil temperature (ST) and soil bulk density (SBD) were significantly lower than under the SF and SW aspects. Under all slope aspects, however, SOC, STN, and soil total phosphate in the top 0.2 m of topsoil accounted for about 60% of its total quantity, to a soil depth of 0.6 m. The plant communities on the SF and SW slopes were found to be primarily composed of Poa pratensis, Potentilla anrisena, and Carex aridula. In contrast, the plant community on the NW slope was mainly composed of Kobresia humilis, Carex crebra, and Potentilla bifurca, while on the NF slope it was mainly composed of Picea crassifolia, Carex scabrirostris, and Polygonum macrophyllum. The order of the influence of environmental factors on species distributions was ST > SBD > sand > STN. Results suggest that the slope aspect has an important role in the regulation of the soil environment and plant assemblages and that ST and SBD were the main factors influencing plant community composition. Furthermore, evidence from this study suggests that these mountains will become increasingly vulnerable to global warming. Thus, the plant community composition on these mountains must be monitored continuously in order to allow for strategic adaptive management.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".