Sudden and sustained response of <i>Acacia koa</i> crop trees to crown release in stagnant stands
Bibliographic record
Abstract
We examined the growth response of Acacia koa Gray (koa) in dense (10 000 trees/ha), ~25-year-old, second-growth stands on Hawai‘i to crown release (thinning) and grass control. Koa diameter increment increased within 2 months of thinning across the range of residual densities (200–900 trees/ha). After 3 years, diameter increment in the most intense thinning treatment was 180% greater than in the control treatment (1.4 vs. 0.5 cm/year); in the least intense thinning treatment diameter increment was 100% greater than the control (1.0 vs. 0.5 cm/year). Koa’s growth response was independent of all measures of tree size (diameter at breast height, height, and leaf area) across the range of tree sizes sampled. A grass control treatment to determine if reduced competition for soil water would improve koa growth showed no improvement in growth response. Koa phyllode δ13C values, which represent an integrated measure of tree water status, showed no evidence of tree water stress (mean δ13C = –28.3‰) and were not significantly different between the grass removal and control treatments. These results demonstrate that koa is capable of responding quickly and positively to crown release despite years of suppressed growth and suggest that landowners interested in managing koa forests may have considerable flexibility in the timing and application of thinning treatments to koa stands.
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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".