Development, Optimization, and Validation of Radium-226 Measurement in Oyster, a Sentinel Organism by Mass Spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
Radium-226 (226Ra) measurement in living organisms, such as the American oyster (Crassostrea virginica), is an analytical challenge: the matrix complexity and the extremely low Ra levels require a purification/preconcentration step prior to its quantification. In this study, 5 g of dry oyster soft tissues and 1.6 g of shell were both mineralized, preconcentrated on an AG50W-X8 and a strontium-specific resin, and measured using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The volumes of digestate used in the method for both matrices were optimized to reach a high preconcentration factor without any risk of oversaturating the columns. Out of the 50 mL of digestates, 48 mL and 2.5 mL were determined as optimal volumes for soft tissues and shell, respectively. To obtain a higher preconcentration factor and a lower limit of quantification (LOQ) for shell samples, three aliquots of 2.5 mL digestate were run on three different sets of resins and, ultimately, combined for Ra analysis using ICP-MS. LOQs of 7.7 and 0.3 fg/g (260 and 11 µBq/g) were achieved for the oyster shell and soft tissues, respectively. The new protocols were applied on relevant samples: oyster soft tissues and shell from New Brunswick, Canada, and different types of reference materials, such as IAEA-470, oyster soft tissue and IAEA-A-12, and animal bones. 226Ra recovery of 105 ± 3% (n = 6) was achieved for IAEA-A-12 (animal bones), the closest available reference material to shell with a recommended value for 226Ra. Resin performances were investigated using 226Ra standard solution and real samples: each set of columns could be used more than 100 times without any significant reduction in Ra preconcentration efficiency. Although the method proposed and validated in this work was developed for oysters, it could easily be applied to other matrices by adjusting the volume of digestate run on the resins to avoid their oversaturation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it