Determination of Absolute Activities and Neutron Fluence Rates Using a Coincidence Method
Bibliographic record
Abstract
Activation studies are an important tool for nuclear decommissioning. By activating material samples in controlled conditions in a known neutron fluence rate, the expected activity in decommissioning can be estimated. This work focuses on determining the fluence rate of a moderated (α, n) neutron source, a key parameter for performing quantitative activity experiments. The fluence rate is determined by an absolute measurement of the accumulated activity of neutron-activated reference samples with a well-known composition using a βγ-coincidence setup. This method requires both single β- and γ- detection, as well as coincidence data. A main advantage of this technique is that the resulting measurement is mostly independent of individual detector efficiencies. The current setup uses a combination of two detectors, consisting of one γ- and one β-detector, paired with a multi-channel data acquisition system, simultaneously recording hits in the β- and γ-channels including their timestamps. Coincidences are extracted in the offline analysis from the stored data. Having singles and coincident data in one dataset reduces the impact of certain corrections, e.g. dead time. Additionally, Monte Carlo techniques were implemented to assess γ-interactions in the γ-detector and to account for the fraction of fake coincidences. Samples, including aluminum ( 27 Al), gold ( 197 Au), Vanadium ( 51 V), sodium chloride (NaCl), and manganese ( 55 Mn) were activated. Calculated thermal fluence rates from weakly attenuating samples agree within uncertainties, with an average fluence rate of Ψ 0 = (2.58 ± 0.09) × 10 5 cm -2 s -1 .
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".