RADIONUCLIDE CO-PRECIPITATION STUDIES UNDER REPOSITORY
Bibliographic record
Abstract
Co-precipitation of some key elements from spent fuel and simulated fuel (SIMFUEL) supplied by Atomic Energy of Canada, Limited (AECL) Research was studied under simulated repository conditions. Apparent equilibrium concentrations from the spent fuel of actinides (Am, Cm, Pu, and Np) and rare earth elements (Eu) were similar to those encountered in a spent-fuel leaching test. The concentration values were lower than anticipated from the solubility of pure solid phases, e.g., Am(OH)3, Cm(OH)3, Np020H(aged), and Eu(OH)3. Co-precipitation phenomena and the formation of solid solutions are suggested as explanations for these low concentrations. The results indicate that the solution concentrations of these elements encountered in spent-fuel dissolution tests represent empirical upper limits for the given set of experimental conditions. Evaluation of the repository relevance of these empirical findings is not yet possible since the geo-chemical, near-field environment will be quite different from the environment in the present idealised experiments. A tentative approach for modelling the Sr concentration as a function of pH in bentonitic-granitic groundwater will be presented. The comparison of experimental and thermodynamic data supports the idea of possible control by the precipitation of different pure solid phases. As a function of pH values, the controlling phases chosen were: pH 8 calcite.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".