The effect of local stand structure on growth and growth efficiency in heterogeneous stands of ponderosa pine and lodgepole pine in central Oregon
Bibliographic record
Abstract
Basal area and height growth were analyzed for individual trees in uneven-aged ponderosa pine (Pinus ponderosa Dougl. ex Laws.) and lodgepole pine (Pinus contorta Dougl. ex. Loud.) stands in central Oregon. Basal area growth was modeled as a function of other stand and tree variables to address three general objectives: (1) to compare the predictive ability of distance-dependent versus distance-independent stand density variables; (2) to determine the degree to which small trees negatively affect the growth of overstory trees; and (3) to test for differences in growth efficiency between species and between indices of spatial occupancy used to define efficiency (area potentially available, crown projection area, and a surrogate for total tree leaf area). Distance-dependent variables were found to improve growth predictions when added to models with only distance-independent variables, and small trees were found to have a quantifiably negative effect on the growth of larger trees. While volume growth efficiency declined with increasing levels of spatial occupancy for lodgepole pine, ponderosa pine volume growth efficiency was greatest at the highest levels of crown base sapwood area and crown projection area. The behavior in ponderosa pine resulted from the previously recognized correlation between tree height and total leaf area or crown size. The final statistical models distinguished between the positive effect of relative height and the negative effect of increasing tree size.
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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 | 0.001 | 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".