Group selection management in conifer forests: relationships between opening size and tree growth
Bibliographic record
Abstract
Replicated circular openings ranging in size from 0.1 to 1 ha were cleared in 1996 at Blodgett Forest Research Station, California, and planted with seedlings of six native species. After 5 years of postharvest growth, heights were measured and analyzed according to species, opening size, and location within opening. The sequence of mean height from tallest to shortest, according to species, was as follows: giant sequoia (Sequoiadendron giganteum (Lindl.) Buchholz) > incense-cedar (Calocedrus decurrens (Torr.) Florin) > Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) ≈ ponderosa pine (Pinus ponderosa Dougl. ex Laws.) > sugar pine (Pinus lambertiana Dougl.) ≈ white fir (Abies concolor (Gord. & Glend.) Lindl.). To describe the influence of openings size on seedling height, we use an information-theoretic approach to select from competing models that predicted fifth-year height from group selection opening size. Asymptotic fits (modeled with MichaelisMenton curves) were selected for giant sequoia, ponderosa pine, sugar pine, and incense-cedar. Quadratic fits were selected for white fir and Douglas-fir. Linear models predicting increasing growth with opening size were consistently ruled out for all species. Although a marked depression in seedling-height growth occurred along the edges within the openings, mean annual radial increment of the 90-year-old border trees surrounding the openings increased by 30%, compared with other canopy trees in the forested matrix between openings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".