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 article introduces a mathematical theory for estimating the power spectral density (PSD) of a random signal based on low-sampling-rate measurements. We formulate the problem using a mathematical model where an observer sees a discrete-time WSS (wide-sense stationary) random signal x(n) through a bank of measurement devices or sensors. Each sensor outputs a measurement signal v/sub i/(n) whose sampling rate is only a fraction of the sampling rate assumed for the original non-observable signal. Knowing statistics of v/sub i/(n) is not, in general, sufficient to specify the PSD of x(n) uniquely. Therefore, the problem of multirate spectral estimation is mathematically ill-posed. We show that it is possible to convert the multirate spectral estimation problem into a mathematically well-posed one using the maximum entropy principle. Moreover, we obtain a closed-form expression for the PSD estimate that results from applying this principle and show that it is unique.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
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