Regeneration of <i>Cryptomeria japonica</i> on a sloping topography in a cool-temperate mixed forest in the snowy region of Japan
Bibliographic record
Abstract
Cryptomeria japonica D. Don shows a limited distribution on and around ridges in its native habitat. To clarify the regeneration process of this species, we analysed spatial patterns among five size classes on a slope extending from a ridge to a valley bottom, and growth patterns of understorey trees related to their slope position, in a cool-temperate old-growth mixed forest in Japan. Although the largest size-class trees ([Formula: see text]20 cm diameter at breast height (DBH)) were confined to the upper part of the slope, understorey size-class trees ([Formula: see text]50 cm stem length and <10 cm DBH) extended their range below the upper regions by layering. The annual growth of understorey size class stems increased towards the lower slope in relation to the understorey light conditions. However, snow pressure injured understorey trees and killed many regenerating medium-sized trees on the steeply inclined expanding site. These results indicate that increased snow pressure, influenced by slope topography, may inhibit the clonal expansion of C. japonica. We concluded that snow pressure gradient on a sloping topography strongly influences the regeneration success of C. japonica, restricting its distribution to ridges in natural forests in snowy regions.
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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 | 0.000 | 0.000 |
| 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".