A comparative test of the predictive power of neighbourhood models in natural populations of <i>Lasallia pustulata</i>
Bibliographic record
Abstract
Three different neighbourhood models were tested to predict individual performance in 50 natural populations of the saxicolous lichen Lasallia pustulata (L.) Mérat. Mean distance to neighbours was clearly the best predictor, accounting for most of the variation in 70% of the populations. In contrast a model based on the number of neighbours within a circle of fixed radius usually had the lowest predictive power. Polygon areas generated by Dirichlet tessellation had a predictive power slightly less than the nearest neighbour approach. The predictive power of all three neighbourhood models was significantly positively correlated, and the polygon and the nearest neighbour model was strongly so. The differences in predictive power are interpreted as reflecting the degree of realism included in the models. The nearest neighbour approach uses actual distances to neighbours, a fairly direct measure of degree of interference in crowded populations. Tessellation models use these distances to generate semi-empirical areas of influence. In contrast the circle model circumscribes a neighbourhood in an arbitrary and abstract manner, and only secondarily take into account the number of organisms within that area. Considering the comparative merits of the models, it is a paradox perhaps that the most frequently used model in previous studies has been the circle model.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".