Patterns of size variation over time in ponderosa pine stands established at different initial densities
Bibliographic record
Abstract
We used six metrics of size and growth variation (standard deviation (SD), coefficient of variation (CV), skewness coefficient (S), Gini coefficient (G), Lorenz asymmetry coefficient (LAC), and growth dominance coefficient (GD)) to describe changes in two long-term ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) initial spacing trials in Oregon and Washington, USA. Trends were examined over a 35-year time period and across a range of initial stand densities (from 154 to 2470 trees·ha−1) for four measures of tree size: diameter at breast height (dbh, 1.37 m), basal area (BA), height, and volume. Unlike many previous studies of size variation in monospecific stands, our results suggest that variation declined or remained relatively stable for all treatments at both study areas. This suggests that these stands are experiencing size symmetric competition for belowground resources. We found that a combination of metrics is necessary to provide a complete picture of size variability and differentiation in developing stands. We recommend using the CV or G, as there were clear trends with increasing density for all size variables. If the objective of the assessment was to track changes in absolute size within an individual stand, we would recommend using the SD, as there were consistent trends with time for all size variables. S, LAC, and GD may be less suited for comparing differentiation during the early stages of stand development because of a lack of clear trends with stand density and time.
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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 | 0.001 | 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".