<b>A computer program to simplify analysis of mass scan data of organometallic compounds from high‐resolution mass spectrometers</b>
Bibliographic record
Abstract
RATIONALE: Software accompanying high-resolution mass spectrometers, particularly that used for the analysis of organometallic compounds, has lagged the technology of the instruments themselves. We have developed a computer program that partially fills this gap. METHODS: Given the user's expectation for the number of atoms of a target element likely to be in an ion, the program calculates isotopologue mass differences for combinations of that element's isotopes and their expected intensity ratios relative to the most abundant isotopologue. These values are compared with mass differences and intensity ratios found in the experimental mass scan data and these metrics feed into a four-factor scoring model which ranks the ions as to the likelihood of each containing the specified number of the target atoms. The program was tested using experimental data obtained for selenomethionine. RESULTS: Across a broad range of sample concentrations, the program consistently ranked selenomethionine at or near the top of the list of ions that passed the screening and ranking process. Mass scan data files in excess of 24,000 records were analyzed in less than one second. CONCLUSIONS: The program is quick and efficient at scanning voluminous experimental data files for the presence of ions containing the expected number of atoms of a target element. Best results were obtained the scarcer the target element and the more isotopes it comprised. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.027 |
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".