A geometric confidence ellipse approach to the estimation of the ratio of two variables. <i>Statistics in Medicine</i> 2008; <b>27</b>:5956–5974.
Bibliographic record
Abstract
Abstract We are grateful to Dr Etienne Kaelin for pointing out four typographic errors in the equations for this paper. These are as follows: The term xy in equation (1) should be replaced by: Equation (3) should be The first term of the unnumbered equation after equation (3) should be µ y rather than µ x . The correct version of equation (4) is The numerical results of this paper as shown in the various tables are not affected by these errors because correct versions of the equations were used in their calculation. Dr Kaelin has offered to make available an Excel spreadsheet that will carry out the calculations for the method described in this paper. It is available to interested readers by contacting him at Etienne.Kaelin@pmintl.com .
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How this classification was reachedexpand
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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".