The code user effect and the international training Seminar 3D SUNCOP
Bibliographic record
Abstract
Thermal-hydraulic system computer codes are extensively used worldwide for analysis of\n\nnuclear facilities by utilities, regulatory bodies, nuclear power plant designers and vendors,\n\nnuclear fuel companies, research organizations, consulting companies, and technical\n\nsupport organizations. The computer code user represents a source of uncertainty that can\n\ninfluence the results of system code calculations. This influence is commonly known as the\n\n‘user effect’ and stems from the limitations embedded in the codes as well as from the\n\nlimited capability of the analysts to use the codes. Code user training and qualification is an\n\neffective means for reducing the variation of results caused by the application of the codes\n\nby different users. This paper describes a systematic approach to training code users who,\n\nupon completion of the training, should be able to perform calculations making the best\n\npossible use of the capabilities of best estimate codes. In other words, the program aims at\n\ncontributing towards solving the problem of user effect. The 3D S.UN.COP (Scaling,\n\nUncertainty and 3D COuPled code calculations) seminars have been organized as followup\n\nof the proposal to IAEA for the Permanent Training Course for System Code Users [1].\n\nEight seminars have been held at University of Pisa (two in 2004), at The Pennsylvania\n\nState University (2004), at the University of Zagreb (2005), at the School of Industrial\n\nEngineering of Barcelona (January-February 2006), in Buenos Aires, Argentina (October\n\n2006), requested by Autoridad Regulatoria Nuclear (ARN), Nucleoelectrica Argentina S.A\n\n(NA-SA) and Comisión Nacional de Energía Atómica (CNEA), at the College Station,\n\nTexas A&M, (January-February 2007), in Hamilton and Niagara Falls, Ontario (October2007) requested by Atomic Energy Canada Limited (AECL), Canadian Nuclear Society\n\n(CNS) and Canadian Nuclear Safety Commission (CNSC). It was recognized that such\n\ncourses represented both a source of continuing education for current code users and a\n\nmean for current code users to enter the formal training structure of a proposed ‘permanent’\n\nstepwise approach to user training. The 3D S.UN.COP - CANDU 2007 in Canada was\n\nsuccessfully held with the attendance of 33 participants coming from 8 countries and 16\n\ndifferent institutions (universities, vendors and national laboratories). More than 30\n\nscientists (coming from 13 countries and 23 different institutions) were involved in the\n\norganization of the seminar, presenting theoretical aspects of the proposed methodologies\n\nand holding the training and the final examination. A certificate (LA Code User grade) was\n\nreleased to participants that successfully solved the assigned problems. A ninth seminar is\n\ncurrently holding (October 2008) at the Institute for Energy, Joint Research Centre of the\n\nEuropean Commission in Petten (The Netherlands), involving more than 30 scientists\n\nbetween lecturers and code developers(http://dimnp.ing.unipi.it/3dsuncop/2008/index.html)
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 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".