Impact of plot size on individual-tree competition measures for growth and yield simulators
Bibliographic record
Abstract
The study addresses the effect of sample plot size on the bias related to measured stand density. We analyzed the effect of plot size on model coefficients and model performance in the simulation. Alternative growth models were developed for Norway spruce (Picea abies (L.) Karst.) on the basis of data obtained from permanent inventory sample plots of varying size. The competition measures were estimated from small plots with an average radius of 6 m, large plots with an average radius of 10 m, a cluster of three small plots within a stand, and a cluster of three large plots within a stand. The response of the models to competition varied depending on the plot size. Increasing the plot size increased the sensitivity of the models to the variation of overall stand density and the competitive status of a tree. The development of repeatedly measured, unthinned and thinned Norway spruce sample plots was simulated with the models, and the predictions were compared with the observed development. In the unthinned stand, the model with competition measures based on small plots resulted in a higher and more biased prediction of growth and mortality than the models based on larger plots. In the thinned stand, the differences between the models were negligible.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".