Correction to “A Note on One‐Sided Tests with Multiple Endpoints,” by M. D. Perlman and L. Wu; 60, 276–280, March 2004
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The original Tables 1 and 2 in the article contain some errors, mainly due to a bug in our computer code. The revised Tables 1 and 2 are given below. The only changes are the results corresponding to “p = 4, ρ = −0.4” and “p = 4, ρ = −0.8.” Also, in the first paragraph of Section 3, “ρ = 0, 0.4, 0.8, −0.4 and −0.8" should be replaced by “ρ = 0, 0.4, 0.8, −0.15 and −0.3.” Simulation results for the sizes of the tests Note: 1. For p = 2, μ1 = (0, 0), μ2 = (−0.25, 0), μ3 = (−0.5, 1). For p = 4, μ*1 = (0, 0, 0, 0), μ*2 = (−0.5, 0, 0, 0), μ*3 = (−0.5, − 0.5, 0, 0), μ*4 = (−0.5, − 0.5, − 0.5, 0), μ*5 = (−0.5, 2, 2, 2), μ*6 = (−0.5, − 0.5, 2, 2), μ*7 = (−0.5, − 0.5, − 0.5, 2). 2. n1 = n2 = 30, ε1=…=εp = 0.5, and α = 0.05. Simulation results for the powers of the tests Note: 1. For p = 2, μ1 = (−0.25, 1), μ2 = (−0.25, 2), μ3 = (0.2, 0.2), μ4 = (0.5, 0.5). For p = 4, μ*1 = (0, 2, 2, 2), μ*2 = (0, 0, 2, 2), μ*3 = (0, 0, 0, 2), μ*4 = (0.2, 0.2, 0.2, 0.2), μ*5 = (0.5, 0.5, 0.5, 0.5), μ*6 = (0.8, 0.8, 0.8, 0.8). 2. n1 = n2 = 30, ε1=…=εp = 0.5, and α = 0.05. The conclusions of the article remain valid. In fact, the new results more strongly support the original conclusions. We thank Yoshiomi Nakazuru at JAPAN TOBACCO, INC. for pointing out the error.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it