D-optimal Design in Linear Model With Different Heteroscedasticity Structures
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we developed D-optimal design in linear model with two explanatory variables in the presence of heteroscedasticity. A sequential method of getting D-optimal design was adopted. Two different structures were used based on the literatures; it was found that the optimal design takes the extreme values of the design region. The results of simulated data was justified with real life data from the kinematic viscosity of a lubricant, in stokes, as a function of temperature and pressure which was used as discussed in Linssen (1975). The relative efficiency of other designs with respect to D-optimal designs was determined. Three correction methods was adopted from weighted least square method for heteroscedasticity problem, it was found that the correction method tagged HCW1 performed better.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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