Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Singular spectrum analysis (SSA), a linear univariate and multivariate time series technique, is essentially principal component analysis (PCA) applied to the time series and additional copies of the time series lagged by 1 to L-1 time steps. Neural network theory has meanwhile allowed PCA to be generalized to nonlinear PCA (NLPCA). In the paper, NLPCA is further extended to perform nonlinear SSA (NLSSA). First, SSA is applied to the data, then the leading principal components of the SSA are chosen as inputs to an NLPCA network (with a circular node at the bottleneck), which performs the NLSSA by nonlinearly combining all the input SSA modes into a single NLSSA mode. This nonlinear spectral technique allows the detection of highly anharmonic oscillations, as illustrated by a stretched square wave imbedded in white noise, which shows NLSSA to be superior to SSA and classical Fourier spectral analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it