Exposure Factors of Activity Patterns in Environmental Health Risk Assessment
Bibliographic record
Abstract
Because human activities impact the time,location and degree of pollutant exposure,they play a key role in explaining exposure variation.This fact has motivated the collection of activity pattern data for their specific use in exposure assessments.In the end of 1980's,the California Air Resources Board sponsored a state-wide activity pattern study,and in the mid-1990's,NHAPS was conducted.NHAPS(the National Human Activity Pattern Survey) is the largest of these recent efforts,a two-year probability-based telephone survey(n=9 386) of exposure-related human activities in the United States.The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales,particularly for use in probabilistic population exposure models.NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Maryland's Survey Research Center using a computer-assisted telephone interview instrument(CATI) to collect 24-hour retrospective diaries and answers to a number of personal and exposure related questions from each respondent.The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day(i.e.,each microenvironment).Between 340 and 1 713 respondents of all ages were interviewed across the 48 contiguous states.Interviews were completed in 63% of the households contacted.NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles.These proportions are fairly constant across the various regions of the United States and Canada and for the California population.By comparing the activities of behavior model parameters domestic and international,the parameters of activities model of the proposed research and development among Chinese population in future was proposed.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".