Analysis and improvement of asymmetric absorbance peak models for IR spectroscopy
Bibliographic record
Abstract
Access to spectral interpretation software has barriers, including prohibitive cost, poor software support, and little diversity in spectral reference libraries used to identify compounds. OpenSpecy is a free, open-source software tool created expressly to address these obstacles. Despite its success, the limitations and errors of OpenSpecy have not been well-studied. This study created the korepanovupdate R language package, to investigate the mathematical foundations of OpenSpecy, especially regarding the asymmetric lineshape models of Stancik and Brauns used to estimate individual peaks. The need for area normalization functions for Stancik and Brauns’ model was recognized and derived through trial and error in this study. This thesis extends the analysis of Korepanov and Sedlovets’ model and examines the asymmetric lineshape models of both sets of authors. This study lowers the error range for area normalization of Korepanov and Sedlovets asymmetric Gaussian-based lineshape from (-0.230%, 0%] to (- 1.47×10-11%, 3.87×10-11%). Similarly, the Lorentzian-based lineshape’s error range was lowered from (-0.0468%, 0.0164%) to (-5.08×10-12%, 4.80×10-12%). This study also provides the first area normalization of Stancik and Brauns’ asymmetric lineshape model, drastically reducing this model’s Gaussian area estimation error range from (-2.43%, 45.67%) to (-0.372%, 0.398%), and its Lorentzian area estimation error range from [0.00%, 57.84%) to (-0.806%, 0.506%). The results of this study also provide the first estimation methods for peak width (g) and asymmetry (a) parameters in Korepanov and Sedlovets’ asymmetric lineshape model. These estimates enable nonlinear least-squares optimization to accurately fit a peak with Korepanov and Sedlovets’ model. Lastly, it was found that two symmetric peaks can be fit to Korepanov and Sedlovets’ asymmetric model.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".