The NRC sealed water calorimeter: correction factors and performance
Bibliographic record
Abstract
The NRC sealed water calorimeter is based on a design first proposed by Domen at the National Institute of Standards and Technology. Thermistor probes are used to measure the temperature increase at a point in a large water phantom due to energy absorbed from the radiation field. A sealed glass vessel is used to control the water quality near the measuring point. To eliminate convective heat transfer, the calorimeter is operated at 4oC. Apart from correction factors, the absorbed dose to water is obtained from the measured temperature change multiplied by the known specific heat of water. However, there are a number of corrections and perturbations which must be considered. Most of these factors are small and can be estimated accurately. The largest uncertainty (0.3%) is contributed by the heat defect, partly because it depends on knowledge of the radiation chemistry of water, and partly because of the difficulty of preparing high purity aqueous systems. The NRC calorimeter has been used to establish the absorbed dose in a ⁶⁰Co beam and several high energy x-ray beams with an estimated standard uncertainty of about 0.5%. The performance of the calorimeter, as well as some of the implications of the results obtained, will be reviewed.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".