On Bayesian Model Assessment and Choice Using Cross-Validation Predictive Densities: Appendix
Bibliographic record
Abstract
http://www.cs.toronto.edu/~radford/fbm.software.html http://www.ncrg.aston.ac.uk/netlab/ where the #'s are the variance hyperparameters. The conjugate inverse Gamma hyperprior for # j 's is # j Inv-gamma(# 0, j ,# #, j ) The fixed values for the highest level hyperparameters in the case studies were similar to those used in (Neal, 1996, 1998). The hyperpriors for w and w were scaled according to the number of inputs K and hidden units J . Typical values were # #,w 1 0.5 # 0,w 1 (0.05/K . ARD prior was used for input weights j,k 1 ), # w 1 ,# #,w 1 ) # w 1 1 ,# #,w 1 ), where the average scale of the # k is determined by the next level hyperparameters. Sampling of the weights was done with HMC and sampling of the hyperparameters was done with Gibbs sampling. GP We used a simple covariance function producing smooth functions C ij exp p u=1 (x (i) ( j ) u ) # ij J # ij # . Jitter
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.162 | 0.021 |
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".