Improved Equations for the Density Management Diagram Isolines of Ponderosa Pine Stands
Bibliographic record
Abstract
Abstract This study was conducted to improve estimation of concomitant variables for implementation of a stand density management diagram (SDMD) for ponderosa pine (Pinus ponderosa Laws.) in northern California and Oregon. In traditional SDMD, isolines for variables such as stand volume are presented in such a way that uncertainty with estimation is not available. We developed the new top height and stand volume equations, as well as aboveground biomass and percent canopy cover, for building isolines in the SDMD using high-quality data collected from well-managed even-aged stands. The data were selected from the USDA Forest Service’s Pacific Southwest Research Station database. A total of 829 observations (from 113 plots across 15 sites in Oregon and California) were used for model construction. In addition, covariance-variance structures of all of the estimated parameters were provided so that users can evaluate the uncertainty associated with predictions. The model validation results indicated that the predictions made from fixed-effects model forms performed better than the current volume equation of SDMD, as well as those from mixed-effects model forms using the population average effect. The proposed equations provide enhanced predictions and additional useful information about managed ponderosa pine stands, including their uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".