Growth and development of ponderosa pine on sites of contrasting productivities: relative importance of stand density and shrub competition effects
Bibliographic record
Abstract
Effects of stand density and shrub competition on growth and development were compared across a gradient of study sites. Challenge, the most productive site, is located in the foothills of the Sierra Nevada, northern California. Pringle Falls is of intermediate productivity in the rain shadow of the central Oregon Cascades. Trough Springs Ridge is the poorest site with minimally developed soils in California's North Coast Range. Treatments included a minimum of four stand densities, from 150 to 2700 trees·ha–1, in combination with at least no or full shrub removal. Challenge produced almost twice as much tree volume as Pringle Falls, and about three times the volume of Trough Springs Ridge. Regardless of site quality, growth was significantly greater in full shrub removal plots for stand densities <2000 trees·ha–1. After 26–36 years, stand volumes were 25–67 m3·ha–1 (11%–38%) greater at Challenge, 30–33 m3·ha–1 (25%–52%) greater at Pringle Falls, and 27–41 m3·ha–1 (115%–326%) greater at Trough Springs Ridge when shrubs were removed. Periodic volume growth declined substantially during the last 10 years at Challenge and Pringle Falls, regardless of treatment, because of confounding effects of mortality, drought, inter-tree competition, and insect defoliation. Further, the importance of shrub control on growth increment was not evident during the last 10 years at both sites, as tree–shrub competition likely switched to tree–tree competition. On the low quality site, shrub control is critical for stand development.
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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) | 0.001 | 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".