Canopy characteristics and growth rates of ponderosa pine and Douglas-fir at long-established forest edges
Bibliographic record
Abstract
Trees respond to edge-to-interior microclimate differences in fragmented forests. To better understand tree physiological responses to fragmentation, we measured ponderosa pine ( Pinus ponderosa Dougl. ex P. & C. Laws) and Douglas-fir ( Pseudotsuga menziesii (Mirbel) Franco) leaf area, crown ratios, sapwood area, basal area (BA) growth rates, and BA growth efficiency at 23 long-established (>50 year) forest edges in northern Idaho. Trees located at forest edges had more leaf area, deeper crowns, higher BA growth rates, and more sapwood area at breast height than interior trees. Ponderosa pine had significantly higher BA growth efficiency at forest edges than interiors, but Douglas-fir BA growth efficiency did not differ, which may relate to differences in photosynthetic capacity and drought and shade tolerance. Edge orientation affected BA growth efficiency, with higher values at northeast-facing edges for both species. Edge effects were significant even after accounting for variation in stand density, which did not differ between the forest edge and interior. Although edge trees had significantly greater canopy depth on their edge-facing than forest-facing side, sapwood area was evenly distributed. We found no evidence that growing conditions at the forest edge were currently subjecting trees to stress, but higher leaf area and deeper crowns could result in lower tolerance to future drought conditions.
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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 | 0.000 | 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 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".