<scp>MOVER‐R</scp>for Confidence Intervals of Ratios
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 themethod of variance recovery for ratios(MOVER‐R). The method encompasses Fieller's theorem as a special case.
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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 | 0.046 | 0.457 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.091 | 0.048 |
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".