Reconstructing spatial tree point patterns from nearest neighbour summary statistics measured in small subwindows
Bibliographic record
Abstract
Spatial tree data are required for the development of spatially explicit models and for the estimation of summary statistics such as Ripley’s K function. Such data are rare and expensive to gather. This paper presents an efficient method of synthesizing spatial tree point patterns from nearest neighbour summary statistics (NNSS) sampled in small circular subwindows, which uses a stochastic optimization technique based on simulated annealing and conditional simulation. This nonparametric method was tested by comparing tree point patterns, reconstructed from sample data, with the original woodland patterns of three structurally different tree populations. Analysis and validation show that complex spatial woodland structures, including long-range tree interactions, can be successfully reconstructed from NNSS despite the limited range of the subwindows and statistics. The influence of the NNSS varies depending on the woodland under study. In some cases, the sampling results can be improved by reconstruction. Furthermore, it is clearly shown that it is possible to estimate second-order characteristics such as Ripley’s K function from small circular subwindows through the reconstruction technique. The results offer new opportunities for adding value to woodland surveys by making raw data available for further work such as growth projections, visualization, and modelling.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".