Efficient synthetic generation of ecological data with preset spatial association of individuals
Bibliographic record
Abstract
Many experiments cannot feasibly be conducted as factorials. Simulations using synthetically generated data are viable alternatives to such factorial experiments. The main objective of the present research is to develop a methodology and platform to synthetically generate spatially explicit forest ecosystems represented by points with a predefined spatial pattern. Using algorithms with polynomial complexity and parameters that control the number of clusters, the degree of clusterization, and the proportion of nonrandom trees, we show that spatially explicit forest ecosystems can be generated time efficiently, which enables large factorial simulations. The proposed method was tested on 1200 synthetically generated forest stands, each of 25 ha, using 10 spatial indices: Clark–Evans aggregation index; Ripley’s K; Besag’s L; Morisita’s dispersion index; Greig–Smith index; the size dominance index of Hui; index of nonrandomness of Pielou; directional index and mean directional index of Corral–Rivas; and size differentiation index of Von Gadow. The size of individual trees was randomly generated aiming at variograms such as real forests. We obtained forest stands with the expected spatial arrangement and distribution of sizes in less than 1 h. To ensure replicability of the study, we have provided free, fully functional software that executes the stated tasks.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".