A model form for stem taper and volume estimates of Asian white birch (<i>Betula platyphylla</i>): a major commercial tree species of Northeast China
Bibliographic record
Abstract
Stem taper functions are valuable tools for the study of stem profile and predict volume estimates. Such functions have long been analyzed for different birch species in the world. However, Asian white birch (Betula platyphylla Suk.) has not yet been investigated as an individual species in China or abroad. This study presents stem taper and volume equations for B. platyphylla in Northeast China. Eight commonly used taper functions were fit to the data of 615 destructively sampled trees from two regions of Northeast China. A second-order continuous autoregressive error structure was incorporated to address the autocorrelation in the data and to achieve the valid parameter estimation. The condition number was used to detect the multicollinearity in the models. The taper functions were compared in terms of overall evaluation statistics and by examining the box plots of diameter and volume residuals against relative heights and diameter classes. The equations of A. Kozak (2004, For. Chron. 80(4): 507–515; model 2), Z. Fang et al. (2000, For. Sci. 46(1): 1–12), and H. Bi (2000, For. Sci. 46(3): 397–409) were the leading models for diameter and volume estimates. Overall, the equation of Z. Fang et al. (2000, For. Sci. 46(1): 1–12) exhibited the best results for estimating diameter and total and merchantable volumes in terms of statistical performance and lower multicollinearity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".