Abundance and distribution of cavity trees in an old-growth subtropical montane evergreen broad-leaved forest
Bibliographic record
Abstract
We examined the relationship between the density of cavity trees and forest structure characteristics and explored the occurrence of cavity trees among different tree species and diameter breast height (DBH) size in a subtropical evergreen broad-leaved forest in the Ailao Mountains in southwestern China. Cavity trees accounted for 7.9% of living trees and 16.3% of dead trees. Average density of living cavity trees (86.3 trees·ha–1) was 6.9 times that of dead cavity trees. Density of living cavity trees was positively correlated with the density of living trees. Cavity trees showed a skewed distribution among DBH classes that peaked at DBHs of 20–40 cm. Moreover, the probability that a living tree was cavity-bearing was logistically related to DBH. Overall, the likelihood of trees being cavity-bearing differed significantly among species. The proportions of cavity trees among the 23 species having more than 63 trees were positively related to the average DBH and to the largest DBH recorded for each species. We suggest that (1) living tree density is important in determining density of cavity trees and (2) differences in proportion of living cavity trees among species is caused mostly by differences in average DBH of each species.
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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".