Nonlinear height–diameter models for three woody, understory species in a temperate oak forest in Hungary
Bibliographic record
Abstract
Information about the diameter and the height of woody species is fundamental to developing growth and yield models in forest stands. Ten nonlinear height–diameter functions were fitted and evaluated for the site. The dataset consisted of 957 selected individuals of three dominant woody species (Acer campestre L., Acer tataricum L., and Cornus mas L.) and represented a wide range of woody species sizes. Changes in these equations following dieback of oak canopies were analysed. Residual standard error (RSE) results of the two-parameter functions showed that the “Wykoff et al. 1982” and “Bates and Watts 1980 – Ratkowsky 1990” functions had lower RSE values in 1972. After oak decline the “Larson 1986” and “Bates and Watts 1980–Ratkowsky 1990” functions had lower values. The RSE data for the three-parameter functions showed that the “Pearl and Reed 1920” function had fitted RSE values at the start of the long-term study. After the canopy decline function, the “Ratkowsky 1990” function RSE value was lowest for A. campestre and C. mas. “Pearl and Reed 1920” was the best-fitted function for A. tataricum. Height–diameter equations increase our knowledge about the growth of these species, which will enable us to improve management planning in oak forests.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".