Report on Project Action Sheet PP05 task 3 between the U.S. Department of Energy and the Republic of Korea Ministry of Education, Science, and Technology (MEST)
Bibliographic record
Abstract
This report documents the results of Task 3 of Project Action Sheet PP05 between the United States Department of Energy (DOE) and the Republic of Korea (ROK) Ministry of Education, Science, and Technology (MEST) for Support with Review of an ROK Risk Evaluation Process. This task was to have Sandia National Laboratories collaborate with the Korea Institute of Nuclear Nonproliferation and Control (KINAC) on several activities concerning how to determine the Probability of Neutralization, PN, and the Probability of System Effectiveness, PE, to include: providing descriptions on how combat simulations are used to determine PN and PE; comparisons of the strengths and weaknesses of two neutralization models (the Neutralization.xls spreadsheet model versus the Brief Adversary Threat-Loss Estimator (BATLE) software); and demonstrating how computer simulations can be used to determine PN. Note that the computer simulation used for the demonstration was the Scenario Toolkit And Generation Environment (STAGE) simulation, which is a stand-alone synthetic tactical simulation sold by Presagis Canada Incorporated. The demonstration is provided in a separate Audio Video Interleave (.AVI) file.
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.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.194 | 0.094 |
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".