Data Processing in Metabolomics Capillary Electrophoresis–Mass Spectrometry
Bibliographic record
Abstract
Metabolomics data extraction may include peak deconvolution, alignment, and integration. The data extraction from CEMS spectra can usually be completed by a software designed for the extraction of liquid chromatography–mass spectrometry (MS) spectra. The purpose of data preprocessing is to preliminarily adjust the obtained data to facilitate the following statistical analysis. Pre-acquisition normalization is relevant more to experimental setups than data processing, so this chapter discusses post-acquisition normalization, which mainly focuses on the data itself. Statistical analysis is the most important step in the processing of metabolomics data. The chapter also discusses some common statistical methods. One of the most significant differences between the data processing of proteomics and metabolomics is the identification of compounds. Metabolite identification usually starts from searching against databases. The Human Metabolome Database, METLIN database, and MassBank contain comprehensive information for many metabolites, including experimental and predicted MS/MS spectra obtained at multiple collision energies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".