Determination of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mn>17</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mn>0</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math>from published data
Bibliographic record
Abstract
The experimental landscape for the $^{7}\mathrm{B}\mathrm{e}+p$ radiative capture reaction is rapidly changing as new high precision data become available. We present an evaluation of existing data, detailing the treatment of systematic errors and discrepancies, and show how they constrain the astrophysical $S$ factor $({S}_{17})$, independent of any nuclear structure model. With theoretical models robustly determining the behavior of the subthreshold pole, the extrapolation error can be reduced and a constraint placed on the slope of ${S}_{17}$. Using only radiative capture data, we find ${S}_{17}(0)=20.7\ifmmode\pm\else\textpm\fi{}0.6(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.0(\mathrm{syst})\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ $\mathrm{b}$ if data sets are completely independent, while if data sets are completely correlated we find ${S}_{17}(0)=21.4\ifmmode\pm\else\textpm\fi{}0.5(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.4(\mathrm{syst})\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ $\mathrm{b}$. The truth likely lies somewhere in between these two limits. Although we employ a formalism capable of treating discrepant data, we note that the central value of the $S$ factor is dominated by the recent high precision data of Junghans et al. [Phys. Rev. C 68, 065803 (2003)], which imply a substantially higher value than other radiative capture and indirect measurements. Therefore we conclude that further progress will require new high precision data with a detailed error budget.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.035 |
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".