Bibliographic record
Abstract
Mass defect is defined as the difference between a compound's exact mass and its nominal mass. This concept has been increasingly used in mass spectrometry over the years, mainly due to the growing use of high resolution mass spectrometers capable of exact mass measurements in many application areas in analytical and bioanalytical chemistry. This article is meant as an introduction to the different uses of mass defect in applications using modern MS instrumentation. Visualizing complex mass spectra may be simplified with the concept of Kendrick mass by plotting nominal mass as a function of Kendrick mass defect, based on hydrocarbons subunits, as well as slight variations on this theme. Mass defect filtering of complex MS data has been used for selectively detecting compounds of interest, including drugs and their metabolites or endogenous compounds such as peptides and small molecule metabolites. Several strategies have been applied for labeling analytes with reagents containing unique mass defect features, thus shifting molecules into a less noisy area in the mass spectrum, thus increasing their detectability, especially in the area of proteomics. All these concepts will be covered to introduce the interested reader to the plethora of possibilities of mass defect analysis of high resolution mass spectra.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".