Influence of canopy structure on the understory environment in tall, old-growth, conifer forests
Bibliographic record
Abstract
The effect of the spatial distribution of trees and foliage on understory conditions was examined in six tall old-growth forests along the Pacific Coast: two sites each in Washington, Oregon, and California. Detailed field measurements of crown parameters were collected on over 9000 trees encompassing over 14.5 ha in the stands. Crown parameters were used to construct a spatially explicit model useful in analyzing the variability of crown distributions in both vertical and horizontal dimensions. Sapwood measurements of over 400 trees in combination with published equations and 240 hemispherical photos were used to assess leaf area and understory light levels, respectively. Shrub and herb cover was used as a biological indicator of growing conditions in the understory. Although leaf area is often assumed to be correlated with the amount of light penetrating the canopy, this is not the case in tall, old-growth forests. The semivariance of the horizontal distribution of canopy volume was strongly correlated with shrub cover and understory light levels and was an overall predictor of canopy structure. This variability gives rise to potentially higher understory light levels and shrub cover values when compared with a forest lacking this vertical heterogeneity and may allow the stand to support a higher volume of foliage.
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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 | 0.001 |
| 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 | 0.001 | 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".