Strategies for Sensor Deployment to Reduce Bias in Environmental Exposure and Health Studies
Bibliographic record
Abstract
Environmental sensing technologies are rapidly advancing. In particular, low-cost air pollutant measurement and health tracking techniques are emerging and existing techniques are becoming more compact and adapted for field deployment. However, field implementation of environmental sensors has proven challenging across many settings, study sizes and study designs, particularly when multiple stakeholder groups are involved in data collection and their interpretation. The proposed presentation will be the fourth in a symposium on the selection and use of sensing technologies for exposure and health studies and related challenges. This presentation will proceed sessions on sensor performance evaluation and validation processes, and aim to present recent examples of novel use of low-cost sensors for environmental intervention and policy evaluation and epidemiologic research. I will incorporate recent examples from my groups work on the integration of both models and sensors in health and exposure studies of household and ambient air pollution. This presentation will aim to facilitate discussion among symposium attendees about strategies to scale up environmental sensing for application in environmental health studies. In particular it will highlight the measurements trade-offs for researchers to consider in sensor section and implementation, and the implications for measurement error and other types of bias in health studies.
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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 | 0.129 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".