Bibliographic record
Abstract
The recent development of nonlinear time series analysis is primarily due to the efforts to overcome the limitations of linear models such as autoregressive moving-average (ARMA) models of Box and Jenkins (1976) in real applications. Two examples of such limitations are the non-ability to model sudden outbursts and the restriction to symmetry in the sense of reversible processes, whereas many processes observed in reality reveal irreversibility, well-known examples being the sunspot numbers and the Canadian Lynx data, see Tong (1990) for a discussion of these data sets with respect to nonlinearity and irreversibility. The increasing popularity of nonlinear time series models is also attributed to the development of nonlinear and nonparametric regression techniques which provides many useful tools. Advanced computational power and easy-to-use advanced softwares and graphics such as S-Plus(Venables and Ripley (1994)) and XploRe(Hardle, Klinke and Turlach (1995)) contribute to the increasing application of nonlinear time series analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".