Methodology for regional forest reallocation using criteria and indicators of the Montreal Process
Bibliographic record
Abstract
人工林から自然林への再転換を考慮した森林の再配置戦略を設計するための手法として，モントリオール・プロセスの基準・指標に対応した持続的な森林管理に向けた広域ランドスケープデザインのための地域スケールゾーニング手法を提案した．提案したコンセプトに基づいて，茨城県北茨城市および高萩市にまたがる約18,500 haの流域を対象範囲として，実データを用いたゾーニングの実践を試行した．木材生産機能と生物多様性保全機能に着目し，対象流域を構成する80の小集水域に対して森林管理目的（木材生産，生物多様性保全および両者の調和）を設定するゾーニングを行った．木材生産機能に対して林地生産力および台風災害危険度を，生物多様性保全機能（特にγ多様性保全）に対してブナ優占林成立適性に基づく植生タイプを，それぞれ自然立地条件から各小集水域について評価してゾーニングの基準とした．試行をとおして，提案した地域スケールゾーニング手法が合理的かつ効率的な森林配置を設計するための意志決定支援ツールとして有効であることが確認された．
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".