Disposal Pathway for Tritiated Reactive Metals and Tritiated Hydrogen Gas
Bibliographic record
Abstract
Kinectrics and its predecessor company Ontario Hydro Research Division (a division of Ontario Hydro) had a fully operational tritium laboratory on site since the early 1980’s. During those years numerous projects and experiments were undertaken using hydrogen and tritium for the most part. Metals with an affinity for hydrogen are commonly employed as scavengers of hydrogenic gases from process streams or as hydrogen storage mediums. The two most common of these metals used were depleted uranium and a zirconium-iron alloy (SAES St198). The break-up of Ontario Hydro through deregulation activities resulted in the building of a new, smaller, tritium laboratory and the decommissioning of the original tritium laboratory. Decommissioning activities resulted in the need to safely dispose of these reactive metals. Disposal of these metals is not straight forward. For safe, long term, disposal it has been decided to oxidize the metals in a controlled fashion. The oxidized beds, containing the metals, will be sent to a radioactive waste site for long term storage. Options for disposal of tritiated hydrogen gas are presented and discussed. This paper provides a disposal pathway for tritiated reactive metals and hydrogen thereby closing the loop in tritium handling.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".