Can We Avoid the Intruder-State Problems in the State-Universal Coupled-Cluster Approaches While Preserving Size Extensivity?
Bibliographic record
Abstract
Following the analysis of principal bottlenecks in the extension of the single-reference (SR) coupled-cluster (CC) methodology to the multireference (MR) case, we review and discuss some recent developments that facilitate the use of general model spaces (GMSs) within the state universal (SU) or Hilbert space MR CC formalism. The use of a GMS improves our ability to avoid the intruder state problems. This feature is further enhanced by generalizing the idea of the externally corrected (ec) SR CC formalism to the MR situations. In this latter approach we employ the cluster analysis to extract the most important higher-than-pair cluster amplitudes from a suitable set of known wave functions. Similarly to the SR case, the most convenient external source is represented by wave functions that are obtained via a modest size MR configuration interaction (CI), which employs an N-dimensional reference space. The resulting higher-than-pair cluster amplitudes are subsequently used in the SU CCSD method that is based on an M-dimensional GMS avoiding intruders. We discuss general aspects of these developments from various viewpoints and provide selective illustrations of the key concepts and ideas.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".