Bibliographic record
Abstract
For the last two decades, experimental information on nuclear level densities for about 60 different nuclei has been obtained on the basis of the Oslo method. While each of these measurements has been typically compared to one or a few level density models, a global study including all the measurements has been missing. The present study provides a systematic comparison between Oslo data and six global level density models for 42 nuclei for which $s$-wave resonance spacings are also available. We apply a coherent normalization procedure to the Oslo data for each of the six different models, all being treated on the same footing. Our quantitative analysis shows that the constant-temperature model presents the best global description of the Oslo data, closely followed by the mean-field plus combinatorial model and Hartree-Fock plus statistical model. Their accuracies are quite similar, so that it remains difficult to clearly favour one of these models. When considering energies above the threshold where the experimental level scheme is complete, all the six models are shown to lead to rather similar accuracies with respect to Oslo data. The recently proposed shape method can, in principle, improve the situation since it provides an absolute estimate of the excitation-energy dependence of the measured level densities. We show for the specific case of $^{112}\mathrm{Cd}$ that the shape method could exclude the Hartree-Fock plus statistical model. Such an analysis remains to be performed for the bulk of data for which the shape method can be applied to the Oslo measurements before drawing conclusions on the general quality of a given nuclear level density model.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".