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
In the problem of estimating bounded normal means, some improved lower bounds for the minimax quadratic risk are presented. Since these bounds hold for any sample size, not merely asymptotically, we refer to them as “nonasymptotic”. First, we will review and compare some well-known bounds due to van Trees, Chentsov, Bhattacharyya, Kooks, Casella-Strawderman, Ibragimov-Khasminskii, and Donoho. The goal is to obtain a reliable nonasymptotic lower bound for the minimax risk applicable to any sample sizes and — through the well-known method of the hardest one-dimensional subfamily — to related nonparametric estimation problems. A combined lower bound will be proposed and compared to the numerically evaluated minimax risk. This comparison shows that the proposed global bound is about 97% accurate for any sample sizes. The results will be applied to nonparametric estimation of linear functionals in the white Gaussian noise in Part II.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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