Spatial heterogeneity analysis of soil seed bank for degraded grassland Stellera chamaejasme populations based on geostatistics
Bibliographic record
Abstract
Soil seed bank is the material basis for natural regeneration of vegetation; the spatial heterogeneity of soil seed bank is of important significance for understanding the mechanism of population reproduction and regeneration. Based on field survey and geostatistics method,this paper studied the spatial heterogeneity of soil seed bank in four differently-degraded grasslands and its relationship with the ground vegetation of Stellera chamaejasme population in the northern slope of Qilian Mountains,Northwest China. Our results showed that the semivariogram models of soil seed bank in the four degraded grassland gradients were nonlinear,showing aggregated distribution. With the aggravation of grassland degradation,the density and range of soil seed bank increased,the sill and structure proportion showed the trend ofUtype,and the spatial heterogeneity of 76. 88%- 93. 75% was caused by the spatial autocorrelation. Furthermore,in both the no-degradation and heavy-degradation grasslands,the relationship between ground vegetation density and seed bank density of S. chamaejasme population showed a significant positive correlation; in the light- and moderate-degradation grasslands,such relationship did not exist. In the process of grassland degradation,the spatial distribution of soil seed bank was mainly affected by structural factors such as ground vegetation,while grazing and other disturbance factors to a certain extent reduced the spatial autocorrelation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".