Modeling tree leaf area of Chinese fir plantations
Bibliographic record
Abstract
Leaf area is an important ecophysiological variable for quantifying the potential production of trees, since it is closely related to tree growth. However, it is difficult to measure the leaf area completely because of the large number of leaves, so it is particularly important to develop accurate species-specific leaf area models. In this study, using 144 parse trees from 48 plots of different climate zones and ages of Chinese fir, tree leaf area models were developed based on sapwood area at breast height (SABH), diameter at breast height (DBH), and diameter at crown base (DCB), respectively. The results showed that the population-averaged levels of nonlinear mixed-effects (NLME) models were better than the plot-levels and base models, and the leaf area models based on DCB performed the best. Finally, the NLME model (16) based on DCB was used as the final model for tree leaf area of Chinese fir plantations, which was consistent with the pipe model theory. All the variables had certain biological and statistical significance and were easy to obtain in the field work (nondestructive). In addition, this study can also provide a reference for other tree species in predicting tree leaf area.
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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 | 0.001 |
| 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 | 0.000 | 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".