Mass Spectrometry: An Outsourcing Guide
Bibliographic record
Abstract
Abstract Proteomics is the study of the proteins expressed from the genome of a cell or organism. Analytical mass spectrometry has recently become a powerful tool to study a cell or organism's proteome and is the focus of this chapter. Mass spectrometry can be used for both qualitative and quantitative analysis of proteomes. This chapter will focus on qualitative analysis, addressing questions of what proteins are present in a proteome and what post‐translational modifications may be associated with these proteins. The instrumentation required for mass spectrometric analysis is generally not available in a standard research laboratory or as part of an undergraduate laboratory, being associated in general with a core facility. We will not describe here details of specific operation of the instruments. Here we focus on the common types of analysis presently in routine use and on preparation of samples for routine biological mass spectrometric analysis that will allow most laboratories, including undergraduate and graduate teaching laboratories, to prepare and analyze samples in experiments designed to obtain proteomic information. Curr. Protoc. Essential Lab. Tech. 2:12.2.1‐12.1.18. © 2009 by John Wiley & Sons, Inc.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.089 | 0.244 |
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".