Internal Calibrant in the Stripping Gas. An Approach to Calibration of Membrane Extraction with a Sorbent Interface
Bibliographic record
Abstract
A new technique for calibration in membrane extraction processes by adding an analytically noninterfering internal calibrant in the stripping gas is described. Membrane extraction with a sorbent interface (MESI) system was used to evaluate this method. During the membrane extraction process, the internal calibrant present in the carrier (stripping) gas and the target analyte present in the sample matrix will permeate simultaneously through the membrane in opposite directions. The changes of accumulation amounts of internal calibrant in the microtrap can be used as a means of calibration to correct the variations of extraction rate due to the variation in environmental factors, such as the sample velocity and the membrane temperature. Thus, this approach should allow for more accurate estimates of the concentrations of target analytes at various sampling or monitoring conditions during field analysis. Finally, a group of selected compounds was employed to test this calibration strategy, and the results indicated the advantages of the proposed approach for on-site analysis.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".