Bibliographic record
Abstract
Overview Science is all about identifying and understanding organized structures or patterns in nature. In this regard, periodic patterns have proven especially important. Nowhere is this more evident than in the field of astronomy. Periodic phenomena allow us to determine fundamental properties like mass and distance, enable us to probe the interior of stars through the new techniques of stellar seismology, detect new planets, and discover exotic states of matter like neutron stars and black holes. Clearly, any fundamental advance in our ability to detect periodic phenomena will have profound consequences in our ability to unlock nature's secrets. The purpose of this chapter is to describe advances that have come about through the application of Bayesian probability theory, and provide illustrations of its power through several examples in physics and astronomy. We also examine how non-uniform sampling can greatly reduce some signal aliasing problems. New insights on the periodogram Arthur Schuster introduced the periodogram in 1905, as a means for detecting a periodicity and estimating its frequency. If the data are evenly spaced, the periodogram is determined by the Discrete Fourier Transform (DFT), thus justifying the use of the DFT for such detection and measurement problems. In 1965, Cooley and Tukey introduced the Fast Discrete Fourier Transform (FFT), a very efficient method of implementing the DFT that removes certain redundancies in the computation and greatly speeds up the calculation of the DFT.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".