Bibliographic record
Abstract
This communication considers perturbative effects on 2-level and 3-level stochastic resonant (SR) quantizers. Such quantizers are briefly reviewed in the small input signal-to-noise ratio (SNR) limit. First order perturbative corrections to the optimal SNR gain and normalized threshold due to small, non-zero input SNRs and a drift in the noise probability density function (PDF) are derived. The noise PDF is assumed to belong to the family of generalized Gaussians indexed by the parameter p e [1, ∞). For p > 1, it is established that these corrections are: (i) bounded, indicating that SR quantizers are stable to such perturbative effects and (ii) can be evaluated numerically using standard mathematical functions and improper integrals. For p → 1+, the corrections are found to be singular, indicating that regular perturbation theory becomes inapplicable for such PDFs. In the limit of heavy-tailed noise PDFs two important results are as follows: (i) the corrections to the SNR gains of 2-level and 3-level quantizers due to a variation in the PDF are equal; (ii) the correction to the normalized threshold of the 2-level quantizer due to a variation in the PDF vanish, but that of the 3-level quantizer do not, implying that 2-level quantizers are stabler than 3-level quantizers to variations in the PDF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".