Effect of Extraction Procedure and Gas Chromatography Temperature Program on Discrimination of <scp>MDMA</scp> Exhibits
Bibliographic record
Abstract
Analysis of impurities in seized MDMA tablets can be used to determine the synthesis method used and to identify links among exhibits. However, no standardized method exists to generate impurity profiles, limiting comparisons among different laboratories. This research investigated the effect of extraction procedure and gas chromatography temperature program on the resulting impurity profiles. Five exhibits were extracted using liquid–liquid extraction (LLE) and headspace solid-phase microextraction (HS-SPME), then analyzed using two different temperature programs. Profiles were statistically assessed using principal components analysis. While LLE was more reproducible, more compounds were extracted using HS-SPME, thus providing more informative chemical profiles. The longer temperature program (53 min vs. 36 min) allowed greater discrimination of exhibits, due to improved precision as a result of an extended hold time (12 min). This research further highlights the need for standardized extraction and analysis procedures to allow comparison of chemical profiles generated in different laboratories.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".