When Are Random Data Not Random, or Is the PTP Test Useful?
Bibliographic record
Abstract
Recently, empirical evidence was presented that the permutation tail probability (PTP) test has extremely low discriminatory power when assessing character covariance in phylogenetic data based on bootstrap measures of confidence. Here we are concerned with the problem of using one statistical approach, especially when applied to empirical data, to judge the performance of another. Applying an appropriate statistical approach, we statistically demonstrated that the PTP test is extremely weak in detecting the absence of character covariation. In addition, we show that PTP is highly dependent on the number of terminals and the proportion of character states in phylogenetic matrices. In conclusion, we advocate the use of simulation studies when testing the performance of statistical tools applied to phylogenetic data.
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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 | 0.277 | 0.717 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".