Preparation and application of tea to a tritium performance testing programme
Bibliographic record
Abstract
A simple, but novel technique, for adjusting steeps of black tea to produce fluids, which are visually and spectroscopically similar to urine, has been developed at the National Calibration Reference Centre for Bioassay and In Vivo Monitoring in Canada. The method uses scans of absorbance versus wavelength, in the UV-VIS range (200-800 nm) to select diluted tea steeps that simulate urine. Tea solutions (1 and 10 %) were spiked with tritium and distributed to laboratories for performance testing (PT). The PT exercise was done as in a regular bioassay programme. The results showed that all samples satisfied the pass/fail conditions of the S-106 standard of the Canadian Nuclear Safety Commission, suggesting that adjusted tea successfully simulated urine for the tritium PT programmes. Also, since unlike urine whose use may increase the probability of contaminating and transmitting diseases (e.g. hepatitis C), tea is a safer alternative. When needed, it can readily be prepared for the laboratories.
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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 | 0.000 | 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 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".