Bibliographic record
Abstract
Opening lectures: Tools for the analysis of chaotic by H. I. Abarbanel Some comments on nonlinear time series analysis by H. Tong Embeddings, dimension, and system reconstruction: A general approach to predictive and fractal scaling dimensions in discrete-index time series by C. D. Cutler Statistics for continuity and differentiability: An application to attractor reconstruction from time series by L. M. Pecora, T. L. Carroll, and J. F. Heagy Reconstruction of integrate-and-fire dynamics by T. Sauer Surrogate methodology: On the validity of the method of by K.-S. Chan Using surrogate data to calibrate the actual rate of false positives in tests for nonlinearity in time series by J. Theiler and D. Prichard Local Lyapunov exponents: Chaos with confidence: Asymptotics and applications of local Lyapunov exponents by B. A. Bailey, S. Ellner, and D. W. Nychka Estimating local Lyapunov exponents by Z.-Q. Lu and R. L. Smith Long-range dependence: Defining and measuring long-range dependence by P. Hall Modelling nonlinearity and long memory in time series by P. M. Robinson and P. Zaffaroni Data analysis and applications: Ergodic distributions of random dynamical systems by L. M. Berliner, S. N. MacEachern, and C. S. Forbes Detecting structure in noise by L. Borland Characterizing nonlinearity in weather and epilepsy data: A personal view by M. C. Casdagli Assessment of linear and nonlinear correlations between neural firing events by A. Longtin and D. M. Racicot Markov chain methods in the analysis of heart rate variability by S. J. Merrill and J. R. Cochran.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".