One-Calibrant Kinetic Calibration for On-Site Water Sampling with Solid-Phase Microextraction
Bibliographic record
Abstract
The existing solid-phase microextraction (SPME) kinetic calibration technique, using the desorption of the preloaded standards to calibrate the extraction of the analytes, requires that the physicochemical properties of the standard should be similar to those of the analyte, which limited the application of the technique. In this study, a new method, termed the one-calibrant kinetic calibration technique, which can use the desorption of a single standard to calibrate all extracted analytes, was proposed. The theoretical considerations were validated by passive water sampling in laboratory and rapid water sampling in the field. To mimic the variety of the environment, such as temperature, turbulence, and the concentration of the analytes, the flow-through system for the generation of standard aqueous polycyclic aromatic hydrocarbons (PAHs) solution was modified. The experimental results of the passive samplings in the flow-through system illustrated that the effect of the environmental variables was successfully compensated with the kinetic calibration technique, and all extracted analytes can be calibrated through the desorption of a single calibrant. On-site water sampling with rotated SPME fibers also illustrated the feasibility of the new technique for rapid on-site sampling of hydrophobic organic pollutants in water. This technique will accelerate the application of the kinetic calibration method and also will be useful for other microextraction techniques.
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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 | 0.006 | 0.009 |
| 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 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".