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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".