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
Finding parametric models that accurately describe the dependence structure\nof observed data is a central task in the analysis of time series. Classical\nfrequency domain methods provide a popular set of tools for fitting and\ndiagnostics of time series models, but their applicability is seriously\nimpacted by the limitations of covariances as a measure of dependence.\nMotivated by recent developments of frequency domain methods that are based on\ncopulas instead of covariances, we propose a novel graphical tool that allows\nto access the quality of time series models for describing dependencies that go\nbeyond linearity. We provide a thorough theoretical justification of our\napproach and show in simulations that it can successfully distinguish between\nsubtle differences of time series dynamics, including non-linear dynamics which\nresult from GARCH and EGARCH models. We also demonstrate the utility of the\nproposed tools through an application to modeling returns of the S&P 500 stock\nmarket index.\n
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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