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
Introduction to multicanonical Monte Carlo simulations by B. A. Berg MCMC in $I \times J \times K$ contingency tables by F. Bunea and J. Besag Extension of Fill's perfect rejection sampling algorithm to general chains (Extended abstract) by J. A. Fill, M. Machida, D. J. Murdoch, and J. S. Rosenthal Taming zero modes in lattice QCD with the polynomial hybrid Monte Carlo algorithm by K. Jansen Monte Carlo algorithms and non-local actions by A. D. Kennedy Towards a more general Propp-Wilson algorithm: Multistage backward coupling by X.-L. Meng On non-reversible Markov chains by A. Mira and C. J. Geyer Exact sampling for Bayesian inference: Unbounded state spaces by D. J. Murdoch Recent progress on computable bounds and the simple slice sampler by G. O. Roberts and J. S. Rosenthal MCMC methods in statistical mechanics: Avoiding quasi-ergodic problems by S. G. Whittington Layered multishift coupling for use in perfect sampling algorithms (with a primer on CFTP) by D. B. Wilson Introduction to semi Markov chain Monte Carlo by H. Ljung Accelerated simulation of ATM switching fabrics by A. R. Dabrowski, G. Lamothe, and D. R. McDonald Some stratagems for the estimation of time series using the Metropolis method by A. R. Runnalls Monte Carlo study of adsorption of interacting self-avoiding walks by T. Vrbova.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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