A Light-Water Detritiation Project at Chalk River Laboratories
Bibliographic record
Abstract
The NRU reactor rod bays is a large, open pool of water that receives hundreds of fuel rods annually, each carrying a small amount of residual tritiated heavy water. The tritium concentration of the rod bays water has risen over the years, to a level that is of concern to the operations staff and to the environment. The proposed long-term solution is to reduce the rod bays tritium concentration by direct detritiation of the water.The Combined Electrolytic-Catalytic Exchange (CECE) process is well suited to the light-water detritiation problem. With a tritium-protium separation factor greater than five, a CECE detritiation process can easily achieve the eight orders of magnitude separation required to split a tritiated light-water feed into an essentially tritium-free effluent stream and a tritiated heavy water product suitable for recycling through a heavy water upgrader.This paper describes a CECE light-water detritiation process specifically designed to reduce the tritium concentration in the NRU rod bays to an acceptable level. The conceptual design of a 600 Mg/a detritiation process has been developed and is now at the stage of project review and the beginning of detailed design.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".