Single-tree management for high-value timber species in a cool-temperate mixed forest in northern Japan
Bibliographic record
Abstract
High-value hardwood species such as monarch birch (Betula maximowicziana) and castor aralia (Kalopanax septemlobus) are important economic and ecological elements of cool-temperate mixed forests in northern Japan. This article presents the single-tree management system for high-value timber species as practised for 50 years at the University of Tokyo Hokkaido Forest. Nearly 2000 valuable broad-leaved trees meeting the size and quality criteria have been registered as ‘superior trees’, and their status is periodically monitored for timing of harvest. A case study was conducted using 2105 inventory plots to characterize the stand types in which superior trees occur. A total of 57 superior trees of 11 broad-leaved species was found in 2.2% of the inventory plots. The results indicated that superior trees generally grew in mature species-rich stands. Superior trees of some species may have promoted their abundance by dispersing relatively more seeds to the surroundings. Single-tree management facilitates the sustainable use of high-value timber species by explicitly monitoring the numbers, attributes and locations of superior trees, and contributes to conserving stand structural diversity through protection of these large-sized canopy trees, which promotes ecological values such as biomass and carbon storage, species diversity, seed abundance and bird habitat. The production of fancy wood from superior trees earns significant income through extremely high log prices (maximum > 20,000 USD m–3).EDITED BY Nicholas Brokaw
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".