Evaluating and communicating uncertainty in the presence of non-white noise
Bibliographic record
Abstract
Classical uncertainty evaluation uses probability density functions that are both Gaussian and stationary in variance about a mean, which may be drifting. Uncertainty in the drift rate means the full uncertainty propagator must allow for the progressive growth of uncertainty as time passes after a calibration. Additionally, the variance is often unavoidably non-stationary: e.g. its within-group variance is less than its between-group variance. We illustrate how this non-stationarity can be studied by Fourier analysis. In many cases, in addition to classical white noise with its flat power spectrum of fluctuations, 1/f and/or 1/f/sup 2/ noise is revealed. A broad class of non-white noise models, that might be thought to give divergent results, is harnessed to derive its explicit uncertainty propagator to describe the growth of uncertainty with time. The propagator suggests simple ways for checking between-time variances for the presence of random walk noise.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".