Poisson Voronoï tiling for finding clusters in spatial point patterns
Bibliographic record
Abstract
Abstract In forest stand mapping a delineation of spatial compact clusters of trees with similar attributes can improve inventory accuracy and growth and yield predictions. To this end a Poisson Voronoï tiling (PVT) for identifying and delineating clusters (features) in spatial point patterns is proposed. PVT operates on the assumption that the point density in clusters is higher than that outside the clusters. A spatial domain of an observed point pattern is tessellated repeatedly into k (random) Poisson Voronoï cells. An average EM-based likelihood of feature based on observed cell point densities is computed for each point and location of interest. Points and locations of interest are then classified by maximizing a classification likelihood. PVT avoids the need to specify the number of clusters. In a direct comparison with a non-parametric maximum profile likelihood procedure, and a smoothed version of the same, PVT performed well on two artificial point patterns with known feature domain and points, and on two spatial point patterns of first returns from a forest lidar survey on Vancouver Island, British Columbia, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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".