FIRST ATLAS DOMESTIC STANDARD PROBLEM (DSP-01) FOR THE CODE ASSESSMENT
Bibliographic record
Abstract
KAERI has been operating an integral effect test facility, ATLAS (Advanced Thermal-Hydraulic Test Loop for Accident Simulation), for accident simulations of advanced PWRs. Regarding integral effect tests, a database for major design basis accidents has been accumulated and a Domestic Standard Problem (DSP) exercise using the ATLAS has been proposed and successfully performed. The ATLAS DSP aims at the effective utilization of an integral effect database obtained from the ATLAS, the establishment of a cooperative framework in the domestic nuclear industry, better understanding of thermal hydraulic phenomena, and an investigation of the potential limitations of the existing best-estimate safety analysis codes. For the first ATLAS DSP exercise (DSP-01), integral effect test data for a 100% DVI line break accident of the APR1400 was selected by considering its technical importance and by incorporating comments from participants. Twelve domestic organizations joined in this DSP-01 exercise. Finally, ten of these organizations submitted their calculation results. This ATLAS DSP-01 exercise progressed as an open calculation; the integral effect test data was delivered to the participants prior to the code calculations. The MARS-KS was favored by most participants but the RELAP5/MOD3.3 code was also used by a few participants. This paper presents all the information of the DSP-01 exercise as well as the comparison results between the calculations and the test data. Lessons learned from the first DSP-01 are presented and recommendations for code users as well as for developers are suggested.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".