CHAPTER 10. BIBLIOGRAPHY 86 Rosenthal, Jeffrey S. (1995) "Convergence rates of Markov chains", SIAM Review, 87, 387-405.
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 Bayesian networks, London, UCL Press Johnson, Valen E. (1996) "Studying convergence of Markov chain Monte Carlo algorithm using coupled sample paths", J. Amer. Statist. Assoc., 91, 154-166. Neal, Radford M. (1993) "Probabilistic Inference Using Markov Chain Monte Carlo Methods", Technical Report CRG-TR-93-1, Department of Computer Science, Uni- versity of Toronto. Neapolitan, Richard E. (1990) Probabilistic Reasoning in Expert Systems, John Wiley & Sons, Inc. Propp, James G., and Wilson, David B. (1996) "Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics", Random Structures and Algorithms, 9, 223-252. Pearl, Judea (1987) "Evidential Reasoning Using Stochastic Simulation of Causal Models", Artifical Intelligence, 32, 245-257. Pearl, Judea (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, Inc. (lOl,5971 (29.5,543) (lO6,5Oll (88.9,5Oll (118,4791 (161,45o) (32.4,471) (95
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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