Shell-model tests of the bimodal partial state densities in a 2 × 2 partitioned embedded random matrix ensemble
Bibliographic record
Abstract
The mixing of well-separated subspacesof an interacting many-particle system,such as a nucleus with active nucleons distributed in more than one major shell,can be studied usingpartitioned embedded ensembles of random matrices. The bimodalform of partial state densities (one-point functions) predictedearlier for a 2 × 2 partitioned embedded ensemble, whichmay be regarded as a model for the mixing of two well-separated degeneratesubspaces, is tested using nuclear shell-model calculations in the[(ds)6 ⊕ (ds)4 (f7/2)2]J=0,T=0 space. Thetheoretical forms predicted by the binary correlationapproximation theory are in good agreement with the shell-modelresults. This suggests that with suitable extensions it might be feasibleto use the binary correlation method to deal with severalinteracting subspaces involving multimodal distributions.PACS Nos.: 05.30.-d, 05.45.Mt, 24.60.Lz
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".