Modeling the number of the first- and second-order branches within the live tree crown of Korean larch plantations in Northeast China
Bibliographic record
Abstract
Modeling the number of branches in a tree is fundamental for simulating other branch characteristics and crown structure. In this study, a total of 77 Korean larch (Larix olgensis Henry) trees were destructively sampled from plantations in Northeast China. The number of first- and second-order branches was modeled using seven count data models, namely Poisson, negative binomial (i.e., NB, including NB-1, NB-2, and NB-P), and generalized Poisson (i.e., GP, including GP-1, GP-2, and GP-P) regression models. Generalized linear mixed models (GLMMs) were then applied to those models using the sampled trees as the random effects. The results showed that (i) the Poisson regression was preferred for modeling the number of first-order branches; (ii) the GP-1 regression was considered the optimal model for the number of second-order branches; (iii) the significant predictor variables included tree height increment, branch position, relative tree size, mean dominant height, and tree age; (iv) the GLMMs significantly improved both model fit and prediction performance; (v) the prediction accuracy of the GLMMs increased gradually with increasing sample size; and (vi) a relatively small sample size with an appropriate sampling strategy would be adequate to provide a good estimation at a specific crown section.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".