Establishing the fitness for purpose of mass spectrometric methods
Bibliographic record
Abstract
This report is submitted by a working group sponsored by the ASMS Measurements and Standards Committee. The group responded to a 1998 opinion piece dealing with mass spectrometry in trace analysis (Bethem, R. A.; Boyd, R. K. J. Am. Soc. Mass Spectrom. 1998, 9, 643-648) which proposed that the concept of fitness for purpose addresses the needs of a wide range of analytical problems. There is a need to define fitness for purpose within the current context of mass spectrometry and to recommend processes for developing and evaluating methods according to suitability for a particular purpose. The key element in our proposal is for the interested parties to define in advance the acceptable degree of measurement uncertainty and the desired degree of identification confidence. These choices can serve as guideposts during method development and targets for retrospective evaluation of methods. A series of more detailed recommendations are derived from basic principles and also from reviews of current practice. This report highlights some areas where consensus is evident, but also revealed the need for further work in other areas. The recommendations are aimed primarily for the laboratory analyst but we hope they will be accessible to the non-scientist as well. Our goal was to provide a framework that can support informed decisions and foster discussion of the issues, because ultimately it is the responsibility of the analyst to make choices, provide supporting data, and interpret results according to scientific principles and qualified judgment.
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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 | 0.261 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".