Membrane Extraction With a Sorbent Interface and Gas Chromatography for the Characterization of Ethylene in Human Breath
Bibliographic record
Abstract
Membrane extraction with a sorbent interface (MESI) is a sample preparation technique with a rugged and simple design allowing for solvent-free, on-line performance. When coupled to gas chromatography (GC), MESI is extremely promising for the analysis of volatile organic compounds (VOCs), as it is selective and sensitive enough to detect trace levels of analytes. A new calibration method for the MESI technique is presented herein. The aim of this study was to characterize and quantify the breath biomarker ethylene in human subjects. The dominant calibration method was proven true using standard ethylene to verify changes in mass transfer trends, with variations in flow and temperature. Finally, the dominant calibration method was used to determine ethylene levels in real human breath samples from healthy and smoking volunteers. Results were consistent with those reported in literature. These findings suggest that the dominant calibration technique is a useful tool for monitoring ethylene in human breath.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".