Model for Screening-Level Assessment of Near-Field Human Exposure to Neutral Organic Chemicals Released Indoors
Bibliographic record
Abstract
Screening organic chemicals for hazard and risk to human health requires near-field human exposure models that can be readily parametrized with available data. The integration of a model of human exposure, uptake, and bioaccumulation into an indoor mass balance model provides a quantitative framework linking emissions in indoor environments with human intake rates (iRs), intake fractions (iFs) and steady-state concentrations in humans (C) through consideration of dermal permeation, inhalation, and nondietary ingestion exposure pathways. Parameterized based on representative indoor and adult human characteristics, the model is applied here to 40 chemicals of relevance in the context of human exposure assessment. Intake fractions and human concentrations (C(U)) calculated with the model based on a unit emission rate to air for these 40 chemicals span 2 and 5 orders of magnitude, respectively. Differences in priority ranking based on either iF or C(U) can be attributed to the absorption, biotransformation and elimination processes within the human body. The model is further applied to a large data set of hypothetical chemicals representative of many in-use chemicals to show how the dominant exposure pathways, iF and C(U) change as a function of chemical properties and to illustrate the capacity of the model for high-throughput screening. These simulations provide hypotheses for the combination of chemical properties that may result in high exposure and internal dose. The model is further exploited to highlight the role human contaminant uptake plays in the overall fate of certain chemicals indoors and consequently human exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".