Twenty years of community dynamics in a mixed conifer broad-leaved forest under a selection system in northern Japan
Bibliographic record
Abstract
Single-tree selection has been employed widely in northern Japanese mixed forests, but management-induced changes in forests are not well understood. This study examined demographic parameters of major tree species during a 20-year study of a 68 ha stand in which single-tree selection has been conducted since 1971. Results showed that growth and survival of conifers (mostly Abies sachalinensis (Fr. Schm.) Masters) was the most strongly positively affected by the treatment. Nevertheless, recruitment of conifers was not sufficiently improved, suggesting their decreased dominance over the longer term. Instead, shade-intolerant broad-leaved species (mainly Betula ermanii Cham.) will gradually increase because of their higher recruitment rates after the treatment. Shade-tolerant broad-leaved species (mainly Acer mono Maxim. and Tilia japonica (Miq.) Simonkai) appeared to experience the most distinct negative effects, especially on survival. These trends differed markedly from those reported in previous papers concerning partial harvesting systems, which predicted an increase in dominance of shade-tolerant species. The results shown here should be generalized carefully because we have investigated only one stand without repetition of the control area. Nevertheless, trends described in this large-scale, long-term study could provide a basis for simulating stand dynamics. We discussed possible reasons for the observed patterns and provided implications for sustainable management in the region.
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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 | 0.001 | 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".