Evaluation of Potential Mercury Releases from Medical Isotope Waste
Bibliographic record
Abstract
Mercuric (Hg) nitrate is used as a catalyst in the medical isotope production process at Atomic Energy of Canada Limited (AECL) Chalk River Laboratories (Chalk River, ON) to ensure consistent Mo-99 target dissolution. The subsequent high level radiological liquid waste is cemented into stainless-steel pails and shipped to a waste management area for long-term storage. Previous studies have confirmed that Hg tends to bind and precipitate I-131, thus minimizing its release to the environment. In order to assess the situation and evaluate the need for Hg monitoring, environmental media (vegetation, surface soil, groundwater, and air) surrounding this waste storage area were sampled and results were compared with applicable guidelines and/or background areas at AECL and other locations. Mercury in groundwater, surface soil, and vegetation were found to be below applicable environmental guidelines and comparable to background locations. Atmospheric Hg near waste storage was found to be elevated above background, but well below applicable guidelines for continuous monitoring. Concentrations of Hg in air also dissipated quickly and were comparable to background within 60 to 80 m from source. The atmospheric Hg monitor used in this study (TEKRAN 2537B) constitutes part of the custom-built portable TEAMS trailer that was designed to provide Chalk River Laboratories with the capability to measure, monitor and model Hg emissions, along with other radiological and non-radiological contaminants, for a wide range of situations. The trailer can also be easily re-configured to adapt to different monitoring needs.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".