RESULTS OF A PHENOMENA IDENTIFICATION AND RANKING TABLE (PIRT) EXERCISE FOR A SEVERE ACCIDENT IN A SMALL MODULAR HIGH-TEMPERATURE GAS-COOLED REACTOR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it