A randomisation program to compare species‐richness values
Bibliographic record
Abstract
Abstract. Comparisons of biodiversity estimates among sites or through time are hampered by a focus on using mean and variance estimates for diversity measures. These estimators depend on both sampling effort and on the abundances of organisms in communities, which makes comparison of communities possible only through the use of rarefaction curves that reduce all samples to the lowest sample size. However, comparing species richness among communities does not demand absolute estimates of species richness and statistical tests of similarity among communities are potentially more straightforward. This paper presents a program that uses randomisation methods to robustly test for differences in species richness among samples. Simulated data are used to show that the analysis has acceptable type I error rates and sufficient power to detect violations of the null hypothesis. An analysis of published bee data collected in 4 years shows how both sample size and hierarchical structure in sample type are incorporated into the analysis. The randomisation program is shown to be very robust to the presence of a dominant species, many rare species, and decreased sample size, giving quantitatively similar conclusions under all conditions. This method of testing for differences in biodiversity provides an important tool for researchers working on questions in community ecology and conservation biology.
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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 | 0.030 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".