Bibliographic record
Abstract
Abstract Operator formalisms are mathematical recipes for simplifying the manipulations of the density matrix. Because the density matrix is a vital tool in describing and analyzing magnetic resonance experiments, many approaches have been developed. This article gives an overview of a number of different methods: spherical tensors, fictitious spin‐1/2, single‐transition operators, product operators, superspin methods, and others. In principle, they all must give the same answer, because an exact description of magnetic resonance phenomena is usually within reach. The choice of the formalism for the user, therefore, depends on various personal decisions. Among these decisions is the choice between spherical and Cartesian tensors, between Hilbert space and Liouville space, between commutators and matrix elements, and so on. We do not go into the details of any of the formalisms but rather try to compare their approaches at a fairly general level. The quadrupolar echo pulse sequence is used as an example of the application of the formalisms. The aim of this overview is to give readers enough of a picture so that they can make an intelligent choice for themselves. © 2006 Wiley Periodicals, Inc. Concepts Magn Reson Part A 28A: 369–383, 2006
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".