Development and Evaluation of Models for the Relationship between Tree Height and Diameter at Breast Height for Chinese-Fir Plantations in Subtropical China
Bibliographic record
Abstract
Tree diameter at breast height (dbh) and height are the most important variables used in forest inventory and management as well as forest carbon-stock estimation. In order to identify the key stand variables that influence the tree height-dbh relationship and to develop and validate a suit of models for predicting tree height, data from 5961 tree samples aged from 6 years to 53 years and collected from 80 Chinese-fir plantation plots were used to fit 39 models, including 33 nonlinear models and 6 linear models, were developed and evaluated into two groups. The results showed that composite models performed better in height estimate than one-independent-variable models. Nonlinear composite Model 34 and linear composite Model 6 were recommended for predicting tree height in Chinese fir plantations with a dbh range between 4 cm and 40 cm when the dbh data for each tree and the quadratic mean dbh of the stand (Dq) and mean height of the stand (Hm) were available. Moreover, Hm could be estimated by using the formula Hm = 11.707 × l n(Dq)-18.032. Clearly, Dq was the primary stand variable that influenced the height-dbh relationship. The parameters of the models varied according to stand age and site. The inappropriate application of provincial or regional height-dbh models for predicting small tree height at local scale may result in larger uncertainties. The method and the recommended models developed in this study were statistically reliable for applications in growth and yield estimation for even-aged Chinese-fir plantation in Huitong and Changsha. The models could be extended to other regions and to other tree species only after verification in subtropical China.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".