Do smaller trees easily form a ring structure around larger trees in temperate forests?
Bibliographic record
Abstract
Forest spatial structure has always been an important topic of ecological research. Large trees directly affect the spatial patterns in forest stands. In this study, we used the data from seven sample plots in natural mixed forests of Korean pine (Pinus koraiensis Siebold & Zucc.) and broad-leaved trees in Heilongjiang Province, China, to examine the effect of large trees on the spatial distribution of adjacent trees and to explore whether this effect is related to the gap dynamics theory. We classified trees with wide diameter (diameter at breast height (DBH) ≥ 50 cm) as central trees and then surveyed the distribution of adjacent trees around each central tree. The results revealed a ring structure of small trees (size class 2 (5.0 ≤ DBH < 10.0 cm) and size class 3 (10.0 ≤ DBH < 30.0 cm)) surrounding large trees. In the two northern sample plots, the trees formed the ring structures with radii of 4–7 m from the large-diameter trees. In the two central sample plots, the ring structures had the radii of 5–9 m and 5–8 m. Analogously, in the three southern sample plots, the ring structures had the radii of 7–11 m and 6–10 m. The formation of a ring structure is closely related to the competition among individuals, and there is an internal relationship between the formation of this structure and the dynamic theory.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".