Bibliographic record
Abstract
Summary Singular spectrum analysis (SSA) is a method utilized for the analysis of time series arising from dynamical systems. The method is used to capture oscillations from a given time series via the analysis of the eigenspectra of the so-called trajectory matrix. The trajectory matrix is composed of multiple data views. The singular value decomposition (SVD) of the trajectory matrix can be used for rank reduction and noise elimination. We apply SSA in the FX domain and present a comparison with classical FX deconvolution. The algorithm arising from SSA analysis is equivalent to Cadzow FX noise attenuation, a method recently proposed by Trickett (2008). It is important to stress, however, that Cadzow filtering (Cadzow, 1988) is a general framework for noise reduction of signals and images. Cadzow filtering is equivalent to SSA when considering sinusoidal waveforms immersed in additive random noise. The intention of this abstract is to provide a simple explanation of the basic assumptions made in SSA and its application to the modeling of plane waves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".