On the use of the Weibull distribution in modeling and describing diameter distributions of clonal eucalypt stands
Bibliographic record
Abstract
Weibull distributions have been widely used to describe tree stem diameter distributions. However, there is a scarcity of studies that suggest the best Weibull formulation. The parameters of the Weibull distribution are usually predicted by either the parameter prediction method (PPM) or the parameter recovery method (PRM), although other methods have been proposed. Thus, this study aimed to evaluate the performance of eight Weibull formulations and compare methods of parameter prediction to describe diameter distributions of clonal eucalypt stands in Brazil. Data originated from remeasurements of 56 plots at ages 3, 5, and 6 years. Weibull distributions were fitted using the maximum likelihood method and evaluated in a goodness-of-fit indicators ranking. The right-truncated two-parameter formulation showed the best results and was used to evaluate the methods of parameter prediction. Stand attributes showed a strong relationship with shape and scale parameters. Regression models were developed and resulted in accurate estimates using PPM. PRM used a growth and yield system to estimate the stand attributes, followed by the moment-based method. The modified cumulative distribution function regression (CDFR) approach was also evaluated, and it presented the poorest results. Although the PPM showed excellent results, PRM is recommended in older stands with inventory because it implicitly promotes compatibility among stand attributes.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".