Analysis and processing of samples for a carbon-14 monitoring program at a radioactive waste storage site
Bibliographic record
Abstract
A monitoring program was undertaken to determine the levels of airborne radioactive 14CO2 in an operating waste management area, and it its vicinity. As a part of this program, alkaline microporous pellets, recently developed, were used to sample the 14CO2 in air near waste storage structures and near the waste management area. These pellets were never fully characterized for their ability to capture high levels of 14CO2, and the processing and analysis needed to be improved to provide data on recoveries and consistencies, for an eventual method validation, with independent calibrations and standards. The sample analysis scheme also had to accommodate 14CO2 levels varying from near the natural background (250 Bq kg-1 C), to potentially three to four orders of magnitude above this value, near the wastes. The porous alkaline solid pellets were used for the passive capture of airborne 14CO2 over a period of weeks, to a few months. The pellets were processed to release the captured CO2 (14CO2 and 12CO2) into a NaOH solution, which was subsequently analyzed by liquid scintillation. Processing of the pellets yielded a 14C recovery of 96.0 +/- 4.2% and a lower, but consistent total carbon recovery, i.e., 85.9 +/- 2.7 and 86.9 +/- 2.6%, for procedural blanks and standards, respectively. The detection limits for the pellet sampling and processing was sufficient to reach environmental levels. For the higher levels of 14CO2 and for 'spot' sampling, we also used air samples, pumped into a NaOH solution to trap the 14CO2. These NaOH solutions were counted directly for 14C, also by liquid scintillation. The method limits of this latter technique, although much higher than for pellet samples, also achieved the performance objective for detecting airborne 14CO2. Both sampling and processing techniques, when used together, provided sufficient flexibility to be used for low (environmental) levels and high levels, near the wastes.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".