Estimation of DTRF Operational Tritium Inventory Using Cryogenic Distillation Column Temperature
Bibliographic record
Abstract
For safe and efficient operation of the Darlington Nuclear Generating Station’s Tritium Removal Facility (DTRF), it is necessary to track the amount of operational tritium inventory within the DTRF’s process systems. Previous methodology that tracks operational tritium inventory is based on performing a tritium mass balance and does not provide an instantaneous way to determine inventory in the DTRF. The estimate of operational tritium inventory using this method is susceptible to increasing cumulative error of approximately ±2.6% per day as the DTRF continues to operate. Current methodology attempts to compensate for this cumulative error by assuming a constant value for operational tritium inventory whenever Mass 5 is detected by mass spectroscopy of tritium drawoff gas. However, this assumption is flawed and introduces significant error to the estimation of operational tritium inventory. A new method based on temperature of the cryogenic high tritium distillation (HTD) process is proposed which can track operational tritium inventory in a more instantaneous fashion and provides a result with a constant error of ±14% that does not increase over time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".