Density estimation based on <i>k</i>-tree sampling and point pattern reconstruction
Bibliographic record
Abstract
In k-tree sampling, also referred to as point-to-tree distance sampling, the k nearest trees are measured. The problem associated with k-tree sampling is its lack of unbiased density estimators. The presented density estimator based on point pattern reconstruction remedies that shortcoming. It requires the coordinates of all k trees. These coordinates are translated into a simulation window where they remain unchanged. Empirical cumulative distribution functions of intertree and location-to-tree distances estimated from the sample plots are set as target characteristics. Using the idea of simulated annealing, an optimal new tree pattern is constructed in the simulation window outside the k-tree samples. The reconstruction of the point pattern minimizes the contrast between the empirical cumulative distribution functions and their analogs for the simulated pattern. The density estimator is simply the tree density of the optimum pattern in the simulation window. The performance of the reconstruction-based density estimator is assessed for k = 6 and k = 4 based on systematic sampling grids regarding its potential application in forest inventories. Simulations are carried out using real stem maps (covering different stand densities and different types of spatial point patterns, such as regular, clustered, and random) as well as completely random patterns. The new density estimator proves to be empirically superior in terms of bias and root mean squared error compared with commonly used estimators. The reconstruction-based density estimator has biases smaller than 2%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".