Investigation of States Populated in the 102Ru(p,t)100Ru Two Neutron Transfer Reaction
Bibliographic record
Abstract
One of the foremost goals of nuclear physics, within the context of nuclear structure, is to provide an understanding of how nuclei are assembled from the basic constituent building blocks of protons and neutrons. This initiative continues to present as extraordinarily nontrivial in nature, as nuclei are highly unique many-body systems with a complex array of properties. The investigation herein focuses on the study of the structure of 100Ru via the two-neutron transfer reaction, 102Ru(p, t)100Ru, that was performed using the Q3D magnetic spectrograph at the Maier-Leibnitz Laboratory, in Garching, Germany. The experimental procedure employed the use of a 102Ru target which was bombarded with protons, resulting in production of 100Ru via the pick-up of two neutrons from the target. The removal of the pair of particles from the system provides a direct study of the neutron-pair properties of the states that were observed in the reaction, which yields a more robust understanding of the structure of 100Ru. This highly selective reaction strongly favours the population of natural parity states, and as such we performed the 102Ru(p,t) reaction to locate the these states in 100Ru, with a special focus on the excited 0+ states to examine their relative strengths 0+ ground state. Furthermore, the 102Ru(p,t) data can establish the spins of the observed excited states, greater aiding with the structure interpretation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".