Statistical performance of a multicomparison method for generalized species diversity indices under realistic empirical scenarios
Bibliographic record
Abstract
The Pallmann-Scherer test is a promising multicomparison procedure to test statistical hypotheses regarding generalized diversity/entropy indices, such as Tsallis family and Hill numbers (Sq and Hq, respectively), which represent alternative ways of profiling species diversity along a gradient of emphasis on species richness versus evenness in abundance distributions. Given the pressing importance of reliably comparing diversity across ecological communities, and since only a few of such procedures are currently available, knowing its statistical performance under realistic ecological scenarios is of strategic importance. In this paper, we evaluated the performance of the Pallmann-Scherer test using computer simulations of communities following different species-abundance distributions, spatially aggregated as widely observed empirically, and sampled by a commonly used quadrat procedure. We found that the test is very conservative for both Sq and Hq, leading to biased significance levels, with low probabilities of type-I error but high probabilities of type-II error (i.e., low statistical power). Although it should be acknowledged that the current method represents an important starting point, further improvements must be made in order to enhance its power and meet the required standards in comparative studies of diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".