PythoMS: A Python Framework to Simplify and Assist in the Processing and Interpretation of Mass Spectrometric Data
Bibliographic record
Abstract
Mass spectrometric data are copious and generate a processing burden that is best dealt with programmatically. PythoMS is a collection of tools based on the Python programming language that assist researchers in creating figures and video output that is informative, clear and visually compelling. The PythoMS framework introduces a library of classes and a variety of scripts that quickly perform time-consuming tasks: making proprietary output readable; binning intensity vs time data to simulate longer scan times (and hence reduce noise); calculate theoretical isotope patterns and overlay them in histogram form on experimental data (an approach that works even for overlapping signals); render videos that enable zooming into the baseline of intensity vs. time plots (useful to make sense of data collected over a large dynamic range) or that depict the evolution of different species in a time-lapse format; calculate aggregates; and provide a quick first-pass at identifying fragments in MS/MS spectra. PythoMS is a living project that will continue to evolve as additional scripts are developed and deployed.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.034 |
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".