Application of the most frequent value method for $$^{39}$$Ar half-life determination
Bibliographic record
Abstract
Abstract An evaluation method supported by robust statistical analysis was applied to historical measurements of $$^{39}$$ 39 Ar half-life. The method, based on the most frequent value (MFV) approach combined with bootstrap analysis, provides a more robust way to estimate $$^{39}$$ 39 Ar half-life, and results in $$T_{1/2}($$ T 1 / 2 ( MFV $$) = 268.2^{+3.1}_{-2.9}$$ ) = 268 . 2 - 2.9 + 3.1 years with uncertainty corresponding to the 68% confidence level. The uncertainty is a factor of 3 smaller than that of the most precise re-calculated $$^{39}$$ 39 Ar half-life measurements by Stoenner et al. and a factor of 2.7 smaller than that of the adopted half-life value in nuclear data sheets. Recently, the specific activity of the beta decay of $$^{39}$$ 39 Ar in atmospheric argon was measured in several underground facilities. Applying the MFV method to a specific activity of $$^{39}$$ 39 Ar from underground measurements results in $$ SA_{{^{39}\text {Ar}}/\text {Ar}}(\text {MFV}) = 0.966^{+0.010}_{-0.018} \, \, \text {Bq/kg}_{\text {atmAr}}$$ S A 39 Ar / Ar ( MFV ) = 0 . 966 - 0.018 + 0.010 Bq/kg atmAr with uncertainty corresponding to the 68% confidence level. In this paper the method to determine the half-life of $$^{39}$$ 39 Ar using the specific activity of $$^{39}$$ 39 Ar in atmospheric argon is also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".