Highly skewed and heavy-tailed tree diameter distributions: approximation using the gamma shape mixture model
Bibliographic record
Abstract
Modeling of the dynamics of forests with Abies alba Mill. and Fagus sylvatica L. in Central Europe requires, among others, simulation of the diameter at breast height (DBH) distributions. It is particularly difficult to select appropriate theoretical models to stratified mixed stands. The objectives of this study are to (i) investigate the suitability of the gamma shape mixture (GSM) model in which the mixing occurs over the shape parameter to model empirical DBH data being characterized by high asymmetry in the proportion of older to younger trees, (ii) analyze the usefulness of the general Bayesian approach for estimating the unknown parameters of this mixture, and (iii) compare the proposed model with commonly used methods such as the single gamma distribution and the gamma model consisting of two components. The least suitable distribution for DBH distribution modeling was the single gamma distribution. The approximation accuracy of the GSM model and the gamma model consisting of two components was similar, but only the GSM model precisely separated older and younger tree generations. The similarity of the right tails of DBH distributions between empirical data and simulation results was the highest for the GSM model.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".