Software algorithm for automatic interpretation of mass spectra of glycerolipids
Bibliographic record
Abstract
A new software algorithm for automatic interpretation of mass spectra of glycerolipids has been developed. The algorithm utilizes a user-specified list of parameters needed to process the spectra. The compounds in mass spectra are identified according to range of measured m/z values, after which the spectra are automatically corrected by the content of naturally occurring isotopes and ion intensities of identified compounds by response correction factors. Automatic processing of the spectra was shown to be accurate and reliable by testing with numerous spectra of glycerophospholipids obtained by liquid chromatography/electrospray ionization mass spectrometry and by comparing the results with manual interpretation of the spectra. If quantitative analysis using internal standards is performed, all the identified compounds in the sample are quantified automatically. A dilution factor may be defined for each sample and is applied to correct the alterations in sample concentration during sample preparation. Processing of several replicate spectra simultaneously produces mean results with standard deviations. The software may also be used to subtract the results of two analyses and to calculate the mean result of replicate subtractions. The algorithm was shown to save time and labor in repetitive processing of mass spectra of similar type. It may be applied to processing of spectra obtained by various mass spectrometric methods.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".