The Allan Variance - challenges and opportunities
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
The Allan variance has historically been estimated using heterodyne measurement systems, which have low noise and preserve the carrier phase information needed for long-term stability. The single-sideband phase noise has traditionally been estimated using phase detectors that suppress the carrier to achieve even lower noise. The recent development of the direct-digital phase noise measurement technique makes it possible to estimate both statistics accurately and simultaneously from the same time series of the phase. Our comparison of the 3 techniques has revealed several challenges to the accurate estimation of the Allan variance including undesired aliasing, biased estimators, and spurious signal generation. Investigation of these difficulties has led to several opportunities to improve Allan variance estimation, including the ability to estimate the instrumentation noise floor during a measurement and the existence of an optimum measurement bandwidth. In the end, this has led to faster, easier, more reliable, and more accurate measurement methods.
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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 | 0.000 | 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 | 0.001 | 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