Toward developing a direct relation between gross volume increment and stand density
Bibliographic record
Abstract
A general form for expressing gross volume increment in terms of stand density is derived and tested with data from spacing trials in red alder (Alnus rubra Bong.), eastern white pine (Pinus strobus L.), longleaf pine (Pinus palustris Mill.), and loblolly pine (Pinus taeda L.). The equation relates the stand sum of individual-tree volume increment per metre height increment to a power function of quadratic mean diameter times tree density. The proposed equation fit the data best when the model included an intercept. Within each species, the fits were unbiased with respect to the independent variables, plantation age, and site height, and with the exception of the youngest ages for red alder and loblolly pine, they were unbiased with respect to the plot sums of individual-tree volume increment divided by individual height increment. Exponents estimated for quadratic mean diameter for each species ranged from 1.58 to 1.80. The resulting equations indicate a linear relationship between the stand sum of individual-tree volume increment per metre height increment and stand density. Scattergrams of gross-volume increment per hectare per year and stand density can be recovered by multiplying the predicted values of the regressions by Lorey’s height. The regressions support the hypothesis that each metre of height growth produces consistent changes in stem size, regardless of initial tree size, age, or site quality, and implies that the change in stem size is a predictable power function of stem diameter for an individual tree or quadratic mean diameter for a stand.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".