Optimal Exposure Biomarkers for Nonpersistent Chemicals in Environmental Epidemiology
Bibliographic record
Abstract
We discuss considerations that are essential when evaluating exposure to nonpersistent, semivolatile environmental chemicals such as phthalates and phenols (e.g., bisphenol A). A biomarker should be chosen to best represent usual personal exposures and not recent, adventitious, or extraneous exposures. Biomarkers should be selected to minimize contamination arising from collection, sampling, or analysis procedures. Pharmacokinetics should be considered; for example, nonpersistent, semivolatile chemicals are metabolized quickly, and urine is the compartment with the highest concentrations of metabolites. Because these chemicals are nonpersistent, knowledge of intraindividual reliability over the biologic window of interest is also required. In recent years researchers have increasingly used blood as a matrix for characterizing exposure to nonpersistent chemicals. However, the biologic and technical factors noted above strongly support urine as the optimal matrix for measuring nonpersistent, semivolatile, hydrophilic environmental agents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".