Bibliographic record
Abstract
Local tree density around a tree affects tree growth because neighboring trees compete for the same resources. In forestry trees are often sampled by measuring all the trees in sample plots. The total number of the trees in a sample plot or in a larger plot that also encompasses a border zone is often used as the density measurement for all trees in the plot. When the plot density is used as the measurement of local density around a sample tree, the measurement error is correlated both with the measured value and with the true value. Thus none of the standard measurement error assumptions hold. The bias in the estimated density effect is related to the plot size. Assuming random tree locations and a simple linear model including both overall stand density and local density as predictor variables, the bias is analyzed analytically using weighted distributions. The plot size producing the highest coefficient of determination is rather close to the size of the influence zone, but much larger plot sizes are needed for unbiased estimation. It is safe to measure density from a larger plot than that used for sample tree selection. The analysis may give insight for other cases in multilevel modeling where group variables are used to explain individual responses.
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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 | 0.087 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".