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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".