Assessment of radium-226 bioavailability and bioaccumulation downstream of decommissioned uranium operations, using the caged oligochaete (<i>Lumbriculus variegatus</i>)
Bibliographic record
Abstract
The present study investigated the integrated effects of several geochemical processes that control radium-226 ((226) Ra) mobility in the aquatic environment and bioaccumulation in in situ caged benthic invertebrates. Radium-226 bioaccumulation from sediment and water was evaluated using caged oligochaetes (Lumbriculus variegatus) deployed for 10 d in 6 areas downstream of decommissioned uranium operations in Ontario and Saskatchewan, Canada. Measured (226) Ra radioactivity levels in the retrieved oligochaetes did not relate directly to water and sediment exposure levels. Other environmental factors that may influence (226) Ra bioavailability in sediment and water were investigated. The strongest mitigating influence on (226) Ra bioaccumulation factors was sediment barium concentration, with elevated barium (Ba) levels being related to use of barium chloride in effluent treatment for removing (226) Ra through barite formation. Observations from the present study also indicated that (226) Ra bioavailability was influenced by dissolved organic carbon in water, and by gypsum, carbonate minerals, and iron oxyhydroxides in sediment, suggestive of sorption processes. Environmental factors that appeared to increase (226) Ra bioaccumulation were the presence of other group (II) ions in water (likely competing for binding sites on organic carbon molecules), and the presence of K-feldspars in sediment, which likely act as a dynamic repository for (226) Ra where weak ion exchange can occur. In addition to influencing bioavailability to sediment biota, secondary minerals such as gypsum, carbonate minerals, and iron oxyhydroxides likely help mitigate (226) Ra release into overlying water after the dissolution of sedimentary barite. Environ Toxicol Chem 2015;34:507-517. © 2014 SETAC.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".