Viability Assessment of Noble Gas Bundle Tagging for Failed-Fuel Identification in CANDU Reactors
Bibliographic record
Abstract
Current limitations of Canadian Deuterium Uranium (CANDU) reactors to reliably locate defective fuel bundles have created interest in new identification techniques. Noble gas tagging, which would involve the addition of specific combinations of Kr and Xe isotopes to the fuel-to-sheath gap during manufacturing, has the potential to offer a means of locating failed-fuel bundles on power, where the released tag could be measured in the primary heat transport system by mass spectrometry. Moreover, the technique could be of particular interest for demonstration irradiations with new fuel bundle designs. This work outlines preliminary considerations on the applicability of noble gas tagging for CANDU reactors. This assessment involved the determination of suitable tag isotopes, the simulation of the impact of the tag on the thermal performance of a fuel element, and the determination of the detection limit of a quadrupole inductively coupled plasma-mass spectrometer instrument for krypton samples with typical aqueous concentrations in the range of 10-12 to 10-9 (molKr/molH2O).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".