Determination of Spectroscopic Band Shapes Using Second Derivatives, Part I: Theory
Bibliographic record
Abstract
The molecular spectra of water, aqueous solutions, hydrogen-bonded systems, and others have massive bands that contain many overlapping components. To decipher the spectra for molecular interpretation, it is necessary to separate these. Several attempts to do this have been made without clear success. To surmount some of the difficulties, we present a novel method, which consists of quantitatively evaluating the spectral band second-derivative profiles. This aids in the determination of the original band profiles: Gaussian, Lorentzian (Cauchy), and Gauss-Lorentz products. Then the number of components in a massive absorption, their shapes, and their positions can be determined. We tested the usefulness of the method in the visible region using calibration standards: a light emitting diode emission spectrum and a holmium chloride (HoCl2) solution. To verify its utility in the infrared region, we used liquid propanol, liquid acetonitrile, and aqueous acetone.
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How this classification was reachedexpand
Full frame distilled prediction
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) | 0.001 | 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) | 0.006 | 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 teacher head, 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".