Nearest-neighbor contingency table analysis of spatial segregation for several species
Bibliographic record
Abstract
Spatial segregation of species occurs when a species is more likely to be located in the vicinity of conspecifics. This can be investigated by mapping and identifying all locations in a study area, then analyzing the nearest-neighbor contingency table, where each location is classified by its species and the species of its nearest neighbor. Nearest-neighbor contingency tables for two species can be analyzed using the methods in Dixon (1994). Here, I present methods to analyze contingency tables for any number of species. Calculation and interpretation of the multispecies contingency table are illustrated by two examples: spatial segregation of species in a swamp forest, with five types of points (Fraxinus caroliniana, Nyssa sylvatica, Nyssa aquatica, Taxodium disticum, and “other species”), and spatial segregation in the gamodioecious tree Nyssa aquatica, with three types of points (male, female, and juvenile). Two issues that affect the results and their interpretation are the choice of randomization (random labelling or toroidal rotation) and the choice of test (pairwise or multispecies).
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".