Bibliographic record
Abstract
Abstract The Mantel test has been widely used in ecology and evolution, but over the last two decades it has been frequently critiqued because results were inconsistent with expectations and there were issues with Type I (false‐positive) and Type II (false‐negative) error rates. Three‐matrix extensions of the Mantel test have been challenged for similar reasons. Even the null hypotheses underlying the Mantel test have been questioned. As a result, use of the Mantel test and its variants has been discouraged or limited to special situations. Here, we examine Mantel test criticisms including the lack of agreement between traditional variable‐based Pearson correlations (r) and observation‐based Mantel correlations (rm), and the unusual Type I and Type II error rates. We propose an alternate proximity measure that resolves these issues. We use simulations and examples to contrast Mantel results based on Euclidean distance, squared Euclidean distance, and the simple difference (Diff) with traditional bivariate Pearson correlations. We demonstrate that use of the simple difference in Mantel tests can resolve the underlying problems with poor agreement between bivariate Pearson and Mantel correlations, as well as appropriate Type I and Type II errors (i.e., where r = cor(x,y) and rm = cor(dx,dy), if dx = Diff(x) and dy = Diff(y), r = rm). We also show that the simple difference can provide solutions to issues with partial Mantel tests and distance‐based MANOVA. Because our results resolve many of the issues with Mantel tests, we hope that these findings will restore the popularity of the Mantel test.
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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 | 0.032 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".