Combining Headspace Solid-Phase Microextraction with Internal Benchmarking to Determine the Elimination Kinetics of Hydrophobic UVCBs
Bibliographic record
Abstract
Substances classified as unknown or variable composition, complex reaction products or biological origin (UVCB) present a challenge for environmental hazard and risk assessment. Here, we present a novel approach for whole-substance bioconcentration testing applied to cedarwood oil-an essential oil composed of volatile, hydrophobic organic chemicals. The method yields whole-body elimination rate constants for a mixture of constituents. Our approach combines in vivo dietary fish exposure with internal benchmarking and headspace solid-phase microextraction (HS-SPME) equilibrium sampling followed by suspect-screening analysis. We quantified depuration rate constants of 13 individual cedarwood oil constituents based on relative peak areas using gas chromatography (GC) coupled with Orbitrap-mass spectrometry (MS) and GC triple-quadrupole (QqQ)-MS. For seven constituents with available analytical standards, we compared the rate constants to the results obtained from solvent extraction, clean-up, and targeted GC-MS analysis. The HS-SPME sampling approach allowed for automated sample extraction and analyte enrichment while minimizing evaporative losses of the volatile target analytes and reducing matrix interferences from low-volatility organics. The suspect-screening analysis enabled the quantification of constituents without available analytical standards, while the internal benchmarking significantly reduced variability from differences in delivered dose and analytical variability between the samples.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".