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
Abstract Signal processing refers to a variety of operations that can be carried out on a continuous (analog) or discrete (digital) sequence of measurements in order to enhance the quality of information it is intended to convey. In the analog domain, electronic signal processing can encompass such operations as amplification, filtering, integration, differentiation, modulation/demodulation, peakdetection, and analog‐to‐digital (A/D) conversion. Digital signal processing can include a variety of filtering methods (e.g. polynomial least‐squares smoothing, differentiation, median smoothing, matched filtering, boxcar averaging, interpolation, decimation, and Kalman filtering) and domain transformations (e.g. Fourier transform (FT), Hadamard transform (HT), and wavelet transform (WT)). Generally the objective is to separate the useful part of the signal from the part that contains no useful information (the noise) using either explicit or implicit models that distinguish these two components. Signal processing at various stages has become an integral part of most modern analytical measurement systems and plays a critical role in ensuring the quality of those measurements.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.199 | 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