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Record W1985968355 · doi:10.1016/s1044-0305(03)00137-5

Establishing the fitness for purpose of mass spectrometric methods

2003· article· en· W1985968355 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Society for Mass Spectrometry · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsContext (archaeology)Identification (biology)TRACE (psycholinguistics)Management scienceElement (criminal law)Work (physics)PsychologyData scienceChemistryComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.299
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it