Revealing System Dynamics through Decomposition of the Perturbation Domain in Two-Dimensional Correlation Spectroscopy
Bibliographic record
Abstract
A technique is presented to simply and effectively decompose the perturbation domain in two-dimensional (2D) correlation maps calculated on a given set of vibrational spectra. Decomposition of the perturbation domain exposes a wealth of kinetic information complementary to the information extracted from conventional 2D correlation spectroscopy. It is shown that the technique produces "perturbation profile maps" that can be utilized in both the interpretation of the conventional 2D correlation maps and the independent kinetic analysis of the given system. Discrimination between spectral features exhibiting similar, but not identical, dynamics is facilitated by the decomposition, and spectral features exhibiting identical dynamics over the perturbation interval are quickly identified. Spectral features exhibiting similar dynamics over only a sub-range of the full perturbation are also identifiable. Interpretation of phase information illuminated in synchronous and asynchronous maps is simplified. Comparison between similar spectral features present in different samples is facilitated with the technique. The simplicity and ease of implementation of the technique make decomposition of the perturbation domain a valuable addition to the tools available in 2D correlation analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".