Thinning ponderosa pine (<i>Pinus ponderosa</i>) stands reduces mortality while maintaining stand productivity
Bibliographic record
Abstract
We analyzed 45 years of data collected from three ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) levels-of-growing-stock installations in Oregon (OR) and northern California (CA), USA, to determine the effect of stand density regimes on stand productivity and mortality. We found that periodic annual increment (PAI) of diameter, basal area (BA), volume, and aboveground dry mass were significantly related to stand density index (SDI) and stand age at start of the period; the quadratic trends varied among sites. Precipitation departure from the normal for each period explained a significant amount of residual variation in all PAI variables except diameter. BA production did not change significantly as SDI exceeded 270 trees·ha−1 at the OR sites and 320 trees·ha−1 at the CA site. Stand productivity was the highest at Elliot Ranch (CA) and the least at Blue Mountains (OR). A similar trend held in growth efficiency under lower stand densities (SDI < 600). Most of the mortality was caused by Dendroctonus bark beetles in stands that exceeded SDI of 500 trees·ha−1. Limiting SDI was about 900 trees·ha−1, although plots at Elliot Ranch reached much higher than that. The results demonstrate that silvicultural control of stand density can be a powerful tool for reducing bark beetle caused mortality without sacrificing stand productivity.
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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 | 0.000 | 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".