Incorporating stand density effects in modeling the taper of red pine plantations
Bibliographic record
Abstract
Taper models are used to estimate the diameter at any height along the bole of a tree. Individual tree volume can then be calculated based on these diameters and corresponding heights. As tree diameters are affected by stand density, inside- and outside-bark taper models that incorporate stand density information were developed for trees in red pine (Pinus resinosa Aiton) plantations. Data used in this study came from stem analysis on 150 red pine trees sampled from 30 even-aged, monospecific plantations across Ontario, Canada. A nonlinear mixed-effects approach was applied in fitting these taper models. Several forms of stand density were evaluated for both inside- and outside-bark diameters. A combination of stand densities expressed as [Formula: see text] (BA, basal area; TPH, trees per hectare) explained the most variation in stem taper of trees grown in red pine plantations in Ontario. This variable was highly significant in the regression and improved the predictive accuracies of both inside- and outside-bark taper models. The taper models presented here are dimensionally compatible. Therefore, these models are applicable for data using any system of units without adjusting parameter values.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".