Data Quality: Importance of the ‘Before Analysis’ Domain [Theory of Sampling (TOS)]
Bibliographic record
Abstract
ABSTRACT Data Quality: what is it, where does it originate, how does it influence data modelling, what can chemometricians do about it? The ‘before analysis’ domain is prone to sampling errors resulting in uncertainties influencing the quality of both analysis and data analysis/data modelling. Nonrepresentative sampling of heterogeneous materials, batches, lots and process streams ‘before analysis’ contribute significantly to the total measurement uncertainty, MU total = MU sampling + MU analysis . The total sampling error (TSE) can dominate over the total analytical error (TAE) by factors ranging 5, 10 or higher , depending on the degree of material heterogeneity encountered and the specific sampling procedure employed to produce the final analytical aliquot, which is the only material actually analysed. The analytical aliquot is the physical manifestation of transgressing the boundary from the before analysis (sampling) domain to the domain of analysis. It is only possible to guarantee representativity of the analytical aliquot, and thus of the analytical results with respect to the original target batch/lot/process stream, by invoking the necessary sampling domain competence stipulated by theory of sampling (TOS). Primary sampling is the most important stage in the full lot‐to‐analysis pathway, quantitatively dominating MU total (but subsequent subsampling stages can also be significant). If the sources of adverse sampling error effects have not been eliminated, the sampling process is biased and MU total will be unnecessarily inflated. TOS offers ways and means to deal actively with a potential sampling bias (which is fundamentally different from the analytical bias). Overlooking, or deliberately ignoring dealing appropriately with sampling effects constitutes a lack of due diligence, which has critical bearings on the QC/QA demands on both analysis and data analysis/modelling. This article presents all uncertainty contributions in the lot‐to‐analysis‐to‐data modelling pathway, which must be identified and managed, eliminated or maximally reduced, to be able to document a fully minimised MU total . Data analysts/chemometricians are part of a scientific collegium covering all three domains: sampling—analysis—data modelling, which are collectively responsible for ‘data quality’. This comprehensive scope has serious implications for the current PAT paradigm, the foundation of which turns out to need significant reform regarding a key process sampling aspect regardless of whether physical samples, or PAT sensor technology spectra, are extracted/acquired. This article introduces the essential minimum TOS competence that must be mastered by stakeholders from all three domains.
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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 | 0.161 | 0.400 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".