Bayesian and Monte Carlo approaches to estimating uncertainty for the measurement of the bound-state <i>β</i> <sup>-</sup> decay of <sup>205</sup>Tl<sup>81+</sup> *
Bibliographic record
Abstract
Abstract The measurement of the bound-state decay of 205 Tl at the Experimental Storage Ring (ESR) at GSI, Darmstadt, has recently been reported, with substantial impact on the use of 205 Pb as an early Solar System chronometer and on the low-energy measurement of the solar neutrino spectrum via the LOREX project. Owing to the technical challenges in producing a high-purity 205 Tl 81+ secondary beam, a robust statistical method was developed to estimate the variation in the contaminant 205 Pb 81+ produced in the fragmentation reaction, which was subsequently transmitted and stored in the ESR. Here, we show that Bayesian and Monte Carlo methods produce comparable estimates for the contaminant variation, each with unique advantages and challenges given the complex statistical problems for this experiment. We recommend the adoption of such methods in future experiments that exhibit unknown statistical fluctuations.
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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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".