Diameter distribution models for thinned taiwania (<i>Taiwania cryptomerioides</i>) plantations
Bibliographic record
Abstract
Summary Taiwania (Taiwania cryptomerioides), which grows mostly in Taiwan, is a highly valued species and consequently requires intensive management. We developed diameter distribution models that incorporate the effects of thinning at different intensities at different ages to assist forest managers to meet their management objectives. Our data consist of tree diameter at breast height measurements from four thinning treatments (control, light, medium, and heavy thinning) at ages 6, 11, 17, 25, 31 and 40 y. We entertained four potential functions for our distribution model: a truncated normal, a generalised Weibull, a four-parameter logit-logistic (LL), and a two-parameter logit-logistic. The four-parameter LL fonction resulted in the best diameter distribution model for our data. We then modelled the parameters of the fitted LL diameter distribution models as a function of time and thinning regime. This allows the diameter distribution to be reconstructed for various combinations of ages and thinning regimes. The truncated normal function fitted the unthinned diameter distributions well. However, the four-parameter LL function also fitted the unthinned data reasonably well and was also able to capture the effect of thinning on the diameter distributions. We discuss some potential applications of the models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".