Reduction of Chemical Formulas from the Isotopic Peak Distributions of High-Resolution Mass Spectra
Bibliographic record
Abstract
A method has been developed for the reduction of the chemical formulas of compounds in complex mixtures from the isotopic peak distributions of high-resolution mass spectra. The method is based on the principle that the observed isotopic peak distribution of a mixture of compounds is a linear combination of the isotopic peak distributions of the individual compounds in the mixture. All possible chemical formulas that meet specific criteria (e.g., type and number of atoms in structure, limits of unsaturation, etc.) are enumerated, and theoretical isotopic peak distributions are generated for each formula. The relative amount of each formula is obtained from the accurately measured isotopic peak distribution and the calculated isotopic peak distributions of all candidate formulas. The formulas of compounds in simple spectra, where peak components are fully resolved, are rapidly determined by direct comparison of the calculated and experimental isotopic peak distributions. The singular value decomposition linear algebra method is used to determine the contributions of compounds in complex spectra containing unresolved peak components. The principles of the approach and typical application examples are presented. The method is most useful for the characterization of complex spectra containing partially resolved peaks and structures with multiisotopic elements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".