Long-range Estimation of Standing Timber Stock in Western Boreal Forest of Canada Using Airborne Laser Altimetry.
Bibliographic record
Abstract
カナダ西部の亜寒帯林を南北に縦断する600kmのトランセクトを対象に航空レーザー測距を実施して森林の縦断プロフィールを得たうえ, 航跡直下の47林分で林分蓄積を実測するとともに, アロメトリー理論に基づき縦断プロフィール面積の一次関数として蓄積を表す関係式を新たに構築した。この蓄積式の有効性を上記実測値で検証したところ, プロフィール面積に対する蓄積の回帰の決定係数は0.74ときわめて有意であった。実測林分の半数で決定したプロフィール面積に対する蓄積の回帰により推定した蓄積を残り半分の実測蓄積と交互に照合したところ, 推定値と実測値の食い違いが0.9～4.5%に収まり, 蓄積式の適用性につき満足すべき結果が得られた。最後に47林分すべてを用いて回帰式を決定し, トランセクト全長にわたる森林蓄積の分布を推定したところ, 蓄積は平均136m3/haで, 最大値はトランセクトの中央より南側に現れ, 北の亜寒帯林側へは徐々に, 南のプレーリー側へは急激に落ち込むという偏った形をしていることが明らかになった。
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".