Assessing exposure in epidemiologic studies to disinfection by-products in drinking water: report from an international workshop.
Bibliographic record
Abstract
The inability to accurately assess exposure has been one of the major shortcomings of epidemiologic studies of disinfection by-products (DBPs) in drinking water. A number of contributing factors include a) limited information on the identity, occurrence, toxicity, and pharmacokinetics of the many DBPs that can be formed from chlorine, chloramine, ozone, and chlorine dioxide disinfection; b) the complex chemical interrelationships between DBPs and other parameters within a municipal water distribution system; and c) difficulties obtaining accurate and reliable information on personal activity and water consumption patterns. In May 2000, an international workshop was held to bring together various disciplines to develop better approaches for measuring DBP exposure for epidemiologic studies. The workshop reached consensus about the clear need to involve relevant disciplines (e.g., chemists, engineers, toxicologists, biostatisticians and epidemiologists) as partners in developing epidemiologic studies of DBPs in drinking water. The workshop concluded that greater collaboration of epidemiologists with water utilities and regulators should be encouraged in order to make regulatory monitoring data more useful for epidemiologic studies. Similarly, exposure classification categories in epidemiologic studies should be chosen to make results useful for regulatory or policy decision making.
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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 | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".