RESULTS OF A PHENOMENA IDENTIFICATION AND RANKING TABLE (PIRT) EXERCISE FOR A SEVERE ACCIDENT IN A SMALL MODULAR HIGH-TEMPERATURE GAS-COOLED REACTOR
Bibliographic record
Abstract
Canada has attracted specific interest from developers of nonwater-cooled small modular reactor (SMR) technologies, including concepts based on high-temperature gas-cooled reactors (HTGRs). It is anticipated that some research and development (R&D) will be necessary to support safety analysis and licensing of these reactors in Canada. The Phenomena Identification and Ranking Table (PIRT) process is a formalized method in which a panel of experts identifies which physical phenomena are most relevant to the reactor safety analysis and how well understood these phenomena are. The PIRT process is thus a tool to assess current knowledge levels and (or) predictive capabilities of models, thus providing direction to a focused R&D program. This paper summarizes the results of a PIRT process performed by a panel of experts at Canadian Nuclear Laboratories for a limiting or “worst-case” accident scenario at a generic HTGR-type SMR. Suggestions are given regarding the highest priority R&D items to support severe accidents analysis of these reactors.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".