<scp>MOVER‐R</scp>for Confidence Intervals of Ratios
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
Abstract Many parameters of interest in statistical analysis are ratios of two quantities. Confidence limits for a ratio may be obtained by an application of Fieller's theorem, if both the numerator and denominator are means of normal variables. However, ratios of nonnormal quantities are common. Examples include the coefficient of variation (CV), for assessing the reproducibility or reliability of a measurement, and the incremental cost‐effectiveness ratio (ICER), defined in the context of a comparative study as the ratio of the difference in cost to the difference in the treatment effect. This article illustrates how to obtain the confidence limits for a ratio without requiring the numerator and denominator to be means of normal distributions. As the basic idea is to recover the variance estimates from confidence limits for the numerator and denominator, this procedure is referred to as the method of variance recovery for ratios (MOVER‐R). The method encompasses Fieller's theorem as a special case.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it