Evaluating MC&A effectiveness to verify the presence of nuclear materials
Bibliographic record
Abstract
Traditional materials accounting is focused exclusively on the material balance area (MBA), and involves periodically closing a material balance based on accountability measurements conducted during a physical inventory. In contrast, the physical inventory for Los Alamos National Laboratory's near-real-time accounting system is established around processes and looks more like an item inventory. That is, the intent is not to measure material for accounting purposes, since materials have already been measured in the normal course of daily operations. A given unit process operates many times over the course of a material balance period. The product of a given unit process may move for processing within another unit process in the same MBA or may be transferred out of the MBA. Since few materials are unmeasured the physical inventory for a near-real-time process area looks more like an item inventory. Thus, the intent of the physical inventory is to locate the materials on the books and verify information about the materials contained in the books. Closing a materials balance for such an area is a matter of summing all the individual mass balances for the batches processed by all unit processes in the MBA. Additionally, performance parameters are established to measure the program's effectiveness. Program effectiveness for verifying the presence of nuclear material is required to be equal to or greater than a prescribed performance level, process measurements must be within established precision and accuracy values, physical inventory results meet or exceed performance requirements, and inventory differences are less than a target/goal quantity. This approach exceeds DOE established accounting and physical inventory program requirements. Hence, LANL is committed to this approach and to seeking opportunities for further improvement through integrated technologies. This paper will provide a detailed description of this evaluation process.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".