Characterization of diameter distribution data in near-natural forests using the Birnbaum–Saunders distribution
Bibliographic record
Abstract
The purpose of this study is to investigate the suitability of the Birnbaum–Saunders distribution to model diameter at breast height (DBH) distributions of near-natural complex structure silver fir ( Abies alba Mill.) – European beech ( Fagus sylvatica L.) forests. The investigations were carried out in Świętokrzyski National Park, situated in Central Poland. To estimate the parameters of the Birnbaum–Saunders distribution, three methods were used: the maximum likelihood method (MLE) and the mean–mean estimator, the modified moment method (MME), and the graphical method (GME). The empirical DBH distributions in near-natural fir–beech stands, which arose according to the model taking into account the overlapping of fir mortality and beech regeneration, were generally conformed to the Birnbaum–Saunders distribution. In such forests, the Birnbaum–Saunders distribution approximated the empirical DBH distributions more precisely than the Weibull and gamma distributions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".