Human Risk Assessment for Nonylphenol
Bibliographic record
Abstract
This article presents a risk assessment for human exposure to nonylphenol (NP). We critically reviewed and assessed all relevant full-text publications based on a variety of data quality attributes. Two categories of data, environmental monitoring and biomonitoring from exposed individuals, were used to estimate human exposure to NP. Environmental monitoring data included the measurement of NP in food, water, air, and dust. From these data and estimates of human intake rates for the sources, exposures were estimated from each source and source-specific Margins of Exposure (MOEs) calculated. However, the nature of the populations studied prevented the calculation of aggregate exposure calculations from these data. Rather, the most reliable estimates of aggregate exposure to NP were those derived from biomonitoring studies in exposed individuals. Using the daily absorbed dose estimates for NP, MOEs were calculated for these populations. The MOEs were based on the use of a No-Observed-Adverse-Effect-Level (NOAEL) for sensitive toxicological endpoints of interest, that is, systemic and reproductive toxicity from continuous-feeding more than 3.5 generations (13 mg/kg/day). The MOEs were all greater than 1000 (ranging from 2863 to 8.4 × 107), clearly indicating reasonable certainty of no harm for source-specific and aggregate (based on biomonitoring) exposures to NP.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".