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
The Gaussian process is a useful prior on functions for Bayesian regression and classification. Density estimation with a Gaussian process prior has been difficult, however, due to the requirements that densities be nonnegative and integrate to unity. The statistics community has explored the use of a logistic Gaussian process for density estimation, relying on various methods of approximating the normalization constant (e.g. [1, 4]). We propose the Gaussian Process Density Sampler (GPDS), a nonparametric, practical and consistent method of constructing a Markov chain on the properties of a posterior distribution on an unknown density, without approximation. The GPDS is composed of four parts. The first part is a GP-based prior on density functions. We develop an exchangeable procedure for generating exact samples in data space from a common density drawn from this prior. Second, we show that this prior allows practical inference of specific values of the unnormalized density, using the recently-developed technique of exchange sampling [3]. Third, we extend this MCMC algorithm to draw samples from the predictive distribution on data space that arises when the posterior on density functions is integrated out. This is our primary result. Finally, we demonstrate a sampling procedure for inference of the Gaussian process hyperparameters.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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