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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".