Isotopic yield in the cold ternary fission of even–even <sup>250–260</sup>Cf isotopes with <sup>14</sup>C as light charged particle
Bibliographic record
Abstract
Within the unified ternary fission model (UTFM), the 14C accompanied cold ternary fission of even–even 250–260Cf isotopes has been studied, in which the interacting potential barrier is taken as the sum of Coulomb and proximity potential. In the 14C accompanied ternary fission of even–even 250–260Cf isotopes, the highest yield is obtained for the fragment combination with doubly magic nuclei 132Sn (N = 82, Z = 50) as the heavier fragment. The presence of doubly magic nuclei 132Sn plays a crucial role for the most favorable splitting in the ternary fission process of even–even 250–260Cf isotopes. It should be noted that the most favorable fragment combinations obtained using UTFM in the ternary fragmentation of 252Cf are found to be the same as that obtained using the triple gamma coincidence technique at the Gammasphere facility. For example, the relative yield found for the fragment combinations like 98Sr + 140Xe + 14C, 100Sr + 138Xe + 14C, 102Zr + 136Te + 14C, 104Zr + 134Te + 14C, 106Mo + 132Sn + 14C, and 108Mo + 130Sn + 14C. We would also like to mention that the fragment combination 106Mo + 132Sn + 14C, which possesses the highest yield obtained using our formalism, is found to be in agreement with that observed in the experiment using the triple gamma coincidence technique at the Gammasphere facility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".