The effects of grazing on the spatial pattern of elm ( <i>Ulmus pumila</i> L.) in the sparse woodland steppe of Horqin Sandy Land in northeastern China
Bibliographic record
Abstract
Abstract. The aim of this study was to explore the effects of grazing on the formation of the spatial pattern of elm growth in a sparse woodland steppe. We used a point pattern method to analyze the elm trees within different diameter at breast height (DBH) classes in both grazed and fenced plots, which were established in Horqin Sandy Land of northeastern China. The results showed that, in the grazed plot, the distances where transformation between random and clustered patterns occurred in class 1 (10 cm ≤ DBH ≤ 15 cm) and class 2 (15 cm < DBH ≤ 20 cm) were 2.27 and 2.37 m, respectively. Meanwhile, in the fenced plot, the distances between random and aggregated patterns that occurred in classes 1, 2 and 3 (DBH > 20 cm) were 3.13, 3.13 and 7.85 m, respectively. In the fenced plot, at distances larger than 67.72 m there was a negative association between classes 1 and 2, which was also the case between classes 2 and 3 and between classes 1 and 3 for distances greater than 104.09 and 128.54 m, respectively. Meanwhile, negative associations occurred only at distances larger than 29.38 m in the grazed plot. These findings suggest that grazing reduced the competition intensity between elm trees; and therefore, grazing management could be an effective strategy used to regulate the elm population in the degraded sandy land of northern China.
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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".