The effect of slope aspect on vegetation attributes in a mountainous dry valley, Southwest China
Bibliographic record
Abstract
Slope aspect plays a critical role in influencing vegetation pattern in semiarid area. The dry valleys of the Hengduan Mountains Region, southwestern China, are striking geographical landscape, suffering from severe ecological degradation. Here, we comprehensively investigated how slope aspect affects vegetation attributes in one of these valleys- the dry valley in the upper reaches of Min River. Three sites were selected along the valley and we quantitively examined the vegetation difference between slope aspects at the whole valley scale and each site level. We found significant vegetation differences between slope aspects in species composition, vegetative structure, and biodiversity pattern, which were in accordance with the observed significant difference in soil nutrient. Generally, north-facing slopes are associated with higher biomass, coverage and height, and species diversity than south-facing slopes. We also found between-aspect differences varied among the study sites, resulting in increased biomass, height, and β diversity differences, decreased density and coverage differences, and opposite trend observed in α diversity at relatively wet site. In conclusion, slope aspect had significant effect on vegetation attributes, which was significantly influenced by local climate (aridity) in terms of both strength and direction depending on the specific attributes investigated.
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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 | 0.001 | 0.001 |
| 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".