Mass spectrometry applied to the analysis of estrogens in the environment
Bibliographic record
Abstract
Environmental analytical chemistry has recently changed focus from analysis of non-polar, persistent contaminants (e.g. polychlorinated biphenyls and dioxins) to more polar and labile compounds that interfere with biological processes. For example, natural and synthetic estrogens and their metabolites have been detected in sewage treatment plant effluents at nanogram/liter concentrations that are similar to those at which both total sex reversal and intersex (containing both testes and ova) is induced in fish exposed to these compounds in laboratory experiments. The development of techniques for the analysis of natural and synthetic estrogens in biological fluids (i.e. serum and urine) has been a priority in the biomedical field. However, the recent recognition that estrogen hormones are contaminants in the environment that may contribute to endocrine disruption has focused attention on the need for highly sensitive and specific techniques that are applicable for trace analysis in complex environmental matrices. Three optimized mass spectrometric protocols have been developed for the determination and quantitation of steroid hormones in environmental matrices using gas chromatography/tandem mass spectrometry (GC/MS/MS), liquid chromatography/mass spectrometry selected ion monitoring, (LC/MS - SIM) and liquid chromatography/tandem mass spectrometry (LC/MS/MS). The advantages and disadvantages of each method are presented.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".