Effects of different forest management systems on plant species diversity in a<i>Fagus crenata</i>forested landscape of central Japan
Bibliographic record
Abstract
To clarify how different forest management systems affect the diversity of understory vascular plant species at the plot level and the forest-type level, we examined a forested landscape originally occupied by primary Japanese beech, Fagus crenata Blume, in central Japan. The landscape is currently composed of four types of forest: primary F. crenata forest, shelterwood logged F. crenata forest, abandoned coppice forest, and coniferous plantation. Species richness per plot (α diversity) and in each forest type (γ diversity) and species turnover among plots in each forest type (β diversity) reached their highest values in plantation forests. While the difference in species composition between primary and shelterwood logged forests was not significant, the other pairs of forest types showed significant differences. Ordination analysis revealed that variation in species composition within the plantations seemed to be related to the dominance of naturally regenerated tree species, which reflected the intensity of tending. Although the species composition of less intensively tended plantations was similar to that of abandoned coppice forests that had been repeatedly cut in the past, their species composition differed from that of the primary forests. This suggests that most of the plantation and coppice forests, which were clear-cut at least once, do not revert to primary forest conditions after management is abandoned.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".