Using Simulation to Test the Reliability of Regression Models
Why this work is in the frame
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Bibliographic record
Abstract
In many sciences, it is standard laboratory practice to use a statistical design of experiment and a regressionmodel to study the influence of multiple parameters under a wide range of conditions. The current study aims atinvestigating the reliability of regression models by examining recently published models. Of particular interestare the assumptions that are not robust to violation such as the reliability of measurements, constant variation ofresiduals, and sample size. To test regression models simulation is used to model potential measurement errorand the importance of sample sizes on parameter estimation. The randomly perturbed designs are then usedtogether with associated mathematical models obtained from the original designs to simulate experiments andobtain new regression models. A comparison of the original model to the new model, and various statistical testsare performed to determine how accurate the original parameters have been predicted when exposed to simulatedmeasurement error.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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