Predicting the number of trees in small diameter classes using predictions from a two-parameter Weibull distribution
Bibliographic record
Abstract
Knowing the abundance of small trees is necessary for accurate calculation of gross production, total carbon and/or biomass of forest stands. The abundance of small trees can also be used to predict ingrowth into larger tree diameter classes. We present a method of predicting numbers of trees in small diameter classes using diameter distributions of larger trees in stands. A truncated two-parameter Weibull distribution was fit to large tree diameters (diameter at breast height (d.b.h.) ≥ 9.0 cm). These parameters were then used to predict the number of small trees in d.b.h. classes smaller than 9.0 cm. Three methods of predicting densities of small trees were used: (1) an extrapolation of the truncated Weibull to a full two-parameter Weibull distribution; (2) a modification of the Weibull using an empirical estimate and (3) a combined approach. While the full two-parameter Weibull distribution generally fitted the distribution of small trees, densities were typically under-predicted. The empirical method (i.e. method 2) produced the best predictions of small tree densities, with a root mean square error of 132 trees h−1 (28 per cent of mean small tree density). Overall, predicting the distribution of small trees using the distribution of large trees worked very well in this study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".