A comparison of estimation methods for fitting Weibull and Johnson's <i>S</i><sub>B</sub> distributions to mixed sprucefir stands in northeastern North America
Bibliographic record
Abstract
Four commonly used estimation methods were employed to fit the three-parameter Weibull and Johnson's S B distributions to the tree diameter distributions of natural pure and mixed red spruce (Picea rubens Sarg.) balsam fir (Abies balsamea (L.) Mill.) stands, respectively, in northeastern North America. The results indicated that the Weibull and the Johnson's S B distributions were, in general, equally suitable for modeling the diameter frequency distributions of this forest type, but the relative performance directly depended on the estimation method used. In this study, the linear regression methods for Johnson's S B were found to give the lowest mean Reynolds' error indices. The conditional maximum likelihood for Johnson's S B and the maximum likelihood estimation for Weibull produced comparable results. However, moment- or mode-based methods were not well suited to the observed diameter distributions that were typically positively skewed, reverse-J, and mound shapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".