Determining stem biomass of Pinus massoniana L. through variations in basic density
Bibliographic record
Abstract
Basic density is a key variable with which to express wood properties, but little attention has been paid to basic density traits in determining stem biomass. A total of 108 Pinus massoniana L. trees were selected from six sites in Hunan Province, China. Cross-sectional discs were cut for analysis using the stem analysis method. Results showed that the highest average basic density per stem for the six sites was 509.1 kg m−3 and the lowest 448.9 kg m−3. Basic density was significantly different (P < 0.05) among the sites. A significant effect of tree age on wood density was confirmed. Furthermore, the linear-mixed model was used to predict stem biomass. The allometric equation incorporating basic density at 10% of tree height (Model 4) was compared with the equation using only diameter at breast height and tree height as independent variables. The mean stem biomass predicted by Model 4 (16.5 kg tree−1) was not significantly different from the observed value (15.8 kg tree−1). The results indicated that incorporating basic density as part of the independent variables could improve model fitness.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".