Improved β‐diversity estimators based on multiple‐site dissimilarity: Distinguishing the sample from the population
Bibliographic record
Abstract
Abstract Aim β‐diversity is often measured through incidence‐based dissimilarity indices, such as the Simpson and Sørensen indices. Multiple‐site versions of these indices, which have been recently developed, enhance the accountability of global heterogeneity relative to the original formulations. However, they are known to be sensitive to the number of sites, which hinders the comparison of β‐diversity between two populations of unequal sizes. Moreover, the populations are never completely measured and the dissimilarity must be estimated from a sample of sites. Innovation In this study, we propose adapted multiple‐site versions of the Simpson, Sørensen and nestedness indices that are population size‐independent as well as estimators of these indices. The properties of the indices and their estimators were tested through simulation studies. These simulation studies show that the adapted indices were population size‐independent. The estimators and their estimated standard errors were nearly unbiased for moderate sample sizes ( n ≥ 25). Main conclusions These estimators of these adapted multiple‐site dissimilarity indices now make it possible to compare the β‐diversity of two populations of unequal sizes.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| 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".