Improving systematic predictions of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>β</mml:mi></mml:math>-delayed neutron emission probabilities
Bibliographic record
Abstract
The probability ${P}_{n}$ of emitting a neutron following $\ensuremath{\beta}$ decay is critical in many areas of nuclear science, from understanding nucleosynethesis during the $r$ process to control of reactor power levels and nuclear waste management. As it is not always easy to measure or calculate, indirect empirical approaches have been developed to estimate the ${P}_{n}$ value from the decay ${Q}_{\ensuremath{\beta}}$ value and the neutron separation energy S${}_{n}$. Here, we present a new prescription incorporating also the half-life ${T}_{1/2}$, which correlates the known data better and thus improves an estimation of ${P}_{n}$ when only ${T}_{1/2}$, ${Q}_{\ensuremath{\beta}}$, and S${}_{n}$ are known. This new relation can be used to predict ${P}_{n}$ values for cases where the half-life is known, thus it can be useful in $r$-process network calculations and in modeling advanced fuel cycles.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".