Sampling-Rate Calibration for Rapid and Nonlethal Monitoring of Organic Contaminants in Fish Muscle by Solid-Phase Microextraction
Bibliographic record
Abstract
Solid-phase microextraction (SPME) is a promising technique for determining organic contaminants within biotic systems. Existing in vivo SPME-kinetic calibration (SPME-KC) approaches are unwieldy due to the necessity of predetermining a distribution coefficient for the analyte of interest in the tissue and the preloading of a calibrating compound to the fiber. In this study, a rapid and convenient SPME alternative calibration method for in vivo analysis, termed SPME-sampling rate (SPME-SR) calibration, was developed and validated under both laboratory and field conditions to eliminate such presampling requirements. Briefly, the SPME probe is inserted into tissue, in this study fish dorsal-epaxial muscle, for 20 min allowing the concentrations of target analytes in the fish muscle to be determined by the extracted amount of analyte and the predetermined sampling rates. Atrazine, carbamazepine, and fluoxetine were detected nonlethally in the low ppb levels within fish muscle, with both laboratory and field-derived results obtained by in vivo SPME-KC comparable (within a factor of 1.27) to those obtained by lethal sampling followed by tissue liquid extraction. The technique described in this study represents an important advance which broadens the application of SPME in vivo sampling technology.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".