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
This paper proposes a data-driven rate-optimal procedure for testing serial correlation of unknown form based on modified Hong’s tests (1996). The tests are based on comparison between a kernel-based spectral density estimator with the null spectral density, using a Quadratic norm, Helling metric, and Kullback information criterion respectively. Under the null hypothesis, the asymptotic distributions of our modified tests are N(0,1). The advantages of our procedure are: (1) the choice of the parameter of the kernel is not arbitrary but data-driven; (2) the tests are adaptive and rate optimal in the sense of Horowitz and Spokoiny (2001); (3) the tests detect Pitman local alternatives with rate that can be arbitrary close to n 1/2 . By simulation, we find that our procedure to select the kernel parameter have accurate level and they are more powerful than LM, BP, LB and Hong tests.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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