Overlapped grouping periodogram test for detecting multiple hidden periodicities in mixed spectra
Bibliographic record
Abstract
Hidden periodicity is a featured phenomenon in many area of the real world, for example, in astronomy and climatology. Under white noise assumption, the problem of searching for hidden periodicity has been studied extensively in the literature. However, under mixed spectra, especially when compound periodicities are involved, most of the existing methods lose their efficiencies. An overlapped grouping periodogram (OGP) test is proposed in this paper to detect multiple hidden periodicities in mixed spectra. Its test statistic is proven to converge to Fisher's g‐statistic almost surely. Difficulties arising from the implementation of the OGP test are tackled and an empirical data‐adaptive confidence interval for grouping parameter selection is constructed when red noise is assumed. The large sample properties of the proposed OGP test are studied via Monte Carlo simulations, and the power of the test is further illustrated by applying it to the reanalysis of the monthly international sunspot series. By employing the new method, a 10‐year cycle and a 11‐year cycle are detected respectively in the monthly international sunspot series. These two cycles combined to form a super cycle with a period of about 110 years for the updated estimate to the solar cycle. The goodness of the OGP fit to the sunspot series is examined.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".