Random and Fixed Effects Selection for Weighted Ridge
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
Using penalized profiled log-likelihood and penalized limited profiled log-likelihood, respectively, together with the weighted ridge penalized term, we offer a method in this study for choosing the fixed and random effects in linear mixed models. Then, we use the penalized restricted profiled log-likelihood to perform in the random effects depending on the chosen tuning parameter. Second, we use the penalized profiled log-likelihood to choose the fixed effect parameters. There is no closed-form solution for the choice of the fixed and random effects, hence the Newton-Raphson technique is employed to iteratively estimate the parameters. We use a simulation study to show how well the suggested strategy works. Lastly, we use two separate datasets to use the methods to further evaluate the newly proposed model.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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