Bibliographic record
Abstract
Abstract Analyses of molecular spectra often use the idea of aneffective Hamiltonian, in which the dynamical variables are the degrees of freedom involved in the spectrum, and the effects of the other degrees of freedom are represented by the values of various parameters. However, it may not be possible to determine all these parameters by empirical fits of the spectra because different parameters may make indistinguishable contributions to the eigenvalues of the Hamiltonian. This article reviews a number of examples of such indeterminacies, including applications to vibration–rotation spectra of diatomic molecules, without or with corrections for the breakdown of the Born–Oppenheimer approximations; electron‐spin structure in diatomic molecules; centrifugal distortion in asymmetric‐top molecules; vibration–rotation resonances in polyatomic molecules; vibration–rotation interactions in degenerate vibrational states; and internal rotation. The discussion of these indeterminacies involves unitary transformations that are expanded as infinite series. This procedure may break down if the convergence of the series is slow. The study of near‐singularities of least‐squares matrices associated with the presence of indeterminacies is also discussed.
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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 | 0.032 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".