Identification and Interpretation of Generalized Two-Dimensional Correlation Spectroscopy Features through Decomposition of the Perturbation Domain
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Bibliographic record
Abstract
Generalized two-dimensional correlation spectroscopy offers great scope for revealing the behavior of relationships between components of a system under empirical study. We have developed methods that aid in the interpretation of two-dimensional correlation spectroscopy. These methods include reference patterns for two-dimensional correlation and correlation coefficient maps, their superposition and joint interpretation, and the use of delta functions to decompose them in the perturbation domain. We show how their joint use permits discrimination between similar two-dimensional correlation map features on the basis of different correlation coefficients. We also show how the decomposition of maps into the perturbation domain reflects the dynamic behavior of spectral features over the course of the perturbation and permits discrimination between otherwise highly similar two-dimensional correlation cross-peaks. These approaches simplify the interpretation of two-dimensional correlation spectroscopy maps and facilitate access to their rich information content.
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it