The role of stand density on growth efficiency, leaf area index, and resin flow in southwestern ponderosa pine forests
Bibliographic record
Abstract
We examined the response of growth efficiency (GE), leaf area index (LAI), and resin flow (RF) to stand density manipulations in ponderosa pine ( Pinus ponderosa Dougl. ex Laws.) forests of northern Arizona, USA. The study used a 40 year stand density experiment including seven replicated basal area (BA) treatments ranging from 7 to 45 m2·ha–1. Results were extended to the larger region using published and unpublished datasets on ponderosa pine RF. GE was quantified using basal area increment (BAI), stemwood production (NPPs), or volume increment (VI) per leaf area (Al) or sapwood area (As). GE per Al was positively correlated with BA, regardless of numerator (BAI/Al, NPPs/Al, and VI/Al; r2 = 0.84, 0.95, and 0.96, respectively). GE per As exhibited variable responses to BA. Understory LAI increased with decreasing BA; however, total (understory plus overstory) LAI was not correlated with BA, GE, or RF. Opposite of the original research on this subject, resin flow was negatively related to GE per Al because Al/As ratios decline with increasing BA. BAI, and to a lesser degree BA, predicted RF better than growth efficiency, suggesting that the simplest measurement with the fewest assumptions (BAI) is also the best approach for predicting RF.
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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 | 0.001 | 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".