Efficient Monte Carlo random sample generation through discretization
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
b.1 6.2 6.3 b.4 6.5 6.6Histograms of Equation (6.1) Histograms of ,t..o,m1 from trquation (6.2) Histograms of 1.. o, rn1 from Equation (6.5) Histoglams of a, B, ), rc, ancl d from Bquation (6.7) Histograms of.,'i : I,2,. . ., 10 from Equation (6.8) Histograms of pf;=3, o{:3, and u,j(:3 from Equation 55 57 59 62 66 69 tv List of Tables 5.1 dean ancl Standarcl Deviation of Bctruation (5.1) 5.2 Nlean and Variance of Equation (5.2) 5.3 Ntlean, Standarcl Deviation, and N{odes of Equation (5.3) 5.4 N4ean and Standard Deviation of Equation (5 4) 5.5 Prior ancl Posterior distribution of 1( from Equation (1.4) .5.6 Parameters, Posterior \4eans ancl Stanclarcl Deviations of 13 and ru3 for 1{ : 3 from the Bquation (1.4) 5.7 proc.time$ in Chapter 4 a and B of Equation (6.1) {eans, Standard Devations, and Nlodes of p, o, m1 from Equation (6.2)Nleans and i\,fodes of a, p,7 of Bquation (6.5) Approximatecl NILB, \4eans, Standard Deviations, ancl \docles of n,,0, , a, ancl B from Equation (6.7) .Rates, N4odes, Ndeans, and Standard Deviations of Pumps , i : 3B 6.1 6.2 6.3 6.4 6.5 I,2,. ..,0 from Equation (6.8) Prior and Posterior distribution of 1{ from Bquation (1.4) .Parameters, Posterior Means and Standard Deviations of r3 ancl tu3 for 1( : 3 from the Equation (1.a) 6.8 proc.timeQinChapter6...
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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