Bibliographic record
Abstract
Sample preparation is a critical step in an analytical process. However, approaches to optimizing the associated parameters are often based on trial and error rather than rational scientific methodologies. If an extraction method provides good recovery, it is assumed that it works well and no further consideration is given to the underlying principles driving its performance. Such a perspective suggests that, when it comes to sample preparation, the fundamentals of method optimization are not as important as in other technologies, such as electrochemistry or chromatography. This is the main reason why the fundamentals of sample preparation are not typically covered in analytical chemistry curricula. Throughout my scientific career, I have carefully considered the underlying principles of sample-preparation procedures, which has led to the development of a range of extraction technologies that have been put to practical use in many labs around the world including Solid Phase Microextraction (SPME). Special attention is given in this chapter to the potential benefits of using SPME, such as higher enrichment and better performance in the characterization of complex systems, including in vivo investigations. Furthermore, optimal approaches to addressing challenges such as swelling and saturation effects are also discussed, as such issues can impair accurate quantification. The information about the operational details of SPME provided in this chapter will not only be critical for facilitating its continued evolution, but it will also be an invaluable resource for both SPME users and other scientists interested in gaining greater insight into extraction principles in general.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.016 |
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".