Spatial patterns of overstory trees in late-successional conifer forests
Bibliographic record
Abstract
We analyzed spatial patterns of overstory trees in late-successional Abies amabilis (Dougl. ex Loud.) Dougl. ex J. Forbes forests and late-successional Pseudotsuga menziesii (Mirb.) Franco forests to establish reference spatial patterns for restoration thinning treatments, and to determine whether thinning treatments with minimum intertree spacing rules result in spatial patterns characteristic of late-successional forests. On average, 32.7% of overstory trees in Abies plots and 26.3% of overstory trees in Pseudotsuga plots occurred as members of multitree clusters (groups of trees in which trees are spaced within a specified minimum distance of each other) at a distance of 3.0 and 4.0 m, respectively. Multitree clusters occurred throughout the three Abies plots; the distribution of multitree clusters within the two Pseudotsuga plots was variable. Spatial patterns of overstory trees in late-successional forests were significantly different from those created by simulated restoration thinning treatments. Restoration thinning treatments that release both individual trees and multitree clusters promote characteristic late-successional tree spatial patterns at the within-patch scale (<0.04 ha). This formulation of restoration thinning highlights conservation of existing small-scale (<0.04 ha) spatial heterogeneity within the treatment area, elaborating on current practices that emphasize introduction of spatial heterogeneity at scales of 0.04 ha to 1.0 ha.
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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 | 0.000 | 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".