Gas chromatography − mass spectral characteristics of six pharmacologically active compounds — analytical performance characteristics on a raw sewage impacted water sample
Bibliographic record
Abstract
We have interrogated the characteristics of six pharmacologically active compounds in a hot gas chromatograph injector including some mass spectral characteristics in a quadrupole mass analyzer. The analytes are dimetridazole, metronidazole, chlorpromazine, trimethoprim, sulfamethazine, and dapsone. We have demonstrated the impact of the injector on the conversion efficiency of solvent to vapor in relation to analytes under investigation. The overall analytical performance of key parameters that were scrutinized and tested on a spiked raw sewer water sample using electron ionization (EI), positive chemical ionization (PCI), and negative chemical ionization (NCI) in the full and selected ion monitoring (SIM) scan modes are also presented. These parameters were the instrument detection limits (IDLs), method detection limits (MDLs), linearities, and percent recoveries. Correlation coefficients (R 2 ) were greater than 0.9950 using all ionization and scan modes. Better MDLs were obtained using the SIM mode in all instances. The SIM mode MDLs ranged as follows: EI 0.308–0.711 and PCI 0.656–1.14 mg/L. Extremely good signals were observed in the NCI mode with dimetridazole and metronidazole where MDLs in the SIM mode were estimated to be 0.057 and 0.062 mg/L. Percent relative standard deviations (n = 3) were all less than 5% using EI employing full and SIM scan modes. Recoveries ranged from 55% to 96% in the full scan mode and from 67% to 94% in the SIM mode. Signal losses and ion population ratios in relation to the number of samples, i.e., scan speed and the mass scan range, are also interrogated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".