Development and validation of a retracted thin film solid phase microextraction device for time weighted average monitoring of artificial sweeteners concentration in surface waters
Bibliographic record
Abstract
A retracted thin-film solid phase microextraction device was used as a passive sampler to monitor the time-weighted average concentration of artificial sweeteners (AS) in Grand River, Ontario, Canada. Laboratory and field calibration were performed to determine differences in the estimated sampling rate values. Laboratory sampling rates ranged from 0.0033 mL day -1 for neohesperidin dihydrochalcone to 0.0075 mL day -1 for saccharin. Sampler devices were deployed for 30 days downstream of a municipal wastewater treatment plant in the Grand River, Ontario, Canada. Linear accumulation for four AS was observed (R 2 >0.9363) in the sampling devices over 30 days, and in-situ sampling rates were between 0.0045 ± 0.0002 for sucralose and 0.0070 ± 0.0009 mL day -1 for acesulfame. The estimated sampling rates for the river and laboratory exhibited less than 20% deviation from the theoretical values. Simultaneously, water samples from the Grand River were collected to determine the concentration levels of artificial sweeteners. The concentration of acesulfame, saccharin, cyclamate, aspartame, and sucralose in the Grand River ranged from 0.13 to 26.5 ng mL -1 . The validation results indicated that this device is suitable for long-term monitoring of AS in surface waters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".