Bibliographic record
Abstract
Within the next 50 years, the global demand for tritium will increase with the startup of fusion reactors, while the global supply (currently estimated at 25 kg) slowly dwindles due to its short half-life. Heavy water moderator from CANDU reactors is the only large scale source of tritium that can meet the rising demand. Only two tritium removal facilities are in operation worldwide, while the development of a third is uncertain. Currently, at the Bruce Nuclear Generation Station in Ontario, Canada, the tritiated heavy water is stored and transported to the Darlington Tritium Removal Facility, posing logistical challenges and risks of radioactive exposure during storage and transport. We propose the implementation of a tritium removal facility at the Bruce Nuclear Generation Station to continuously extract tritium from heavy water moderator from the four Bruce C units currently in the early stages of development. The heavy water would undergo electrolysis before being cooled to cryogenic temperatures of about 26–27 K and fed through a cryogenic distillation cascade that would produce gaseous tritium of 99.9% purity at a rate of 178 grams per year. Continuous tritium extraction would also maintain the radioactivity of the CANDU reactor moderator under 10 Ci/kg, an essential benchmark for reactor and environmental safety. Compared to existing tritium removal facilities, this process presents three novel advantages: 1) Direct electrolysis pre-treatment that foregoes the use of complex catalysts; 2) A thermally linked design that utilizes helium refrigerant to provide heating and cooling duty; 3) Optimized cryogenic distillation system that provides tritium product of higher purity that other similar processes. Assuming a tritium sale price of $30,000 per gram and a plant lifetime of 35 years, the tritium removal process presented is not profitable, with an ROI of -6.13% in the third production year and a negative IRR. However, this process design is highly valuable as tritium prices are expected to surge in the next decade as fusion plants reach technological readiness and require tritium to fuel fusion reactors.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.024 |
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; both teacher heads agree on what is shown here.
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".