Developing an Environmental Exposure Data Platform for Urban Health Research
Bibliographic record
Abstract
An important part of the Canadian Urban Environmental Health Research Consortium’s (CANUE) mandate is developing an operational urban environmental exposure data platform in support of population-based health research. An effective data platform is viewed as key to breaking down some of the existing research silos in the environment and health field and enabling investigation of multiple and interacting environmental influences on population health.In this presentation, we focus on summarizing the types of exposure data in our platform and those we are currently working on, along with how we manage data access and provide user support with value-added content, and custom data processing and visualization tools. In the early stages of developing content for the data platform our efforts focused on integrating data from different national-scale data sources already in existence, many of which were publicly available with published supporting documentation. Continuing efforts focus on developing new data metrics and integrating the many regional and city-specific or, so-called long-tailed data that make up most urban data sets.Our early experience in developing an environmental exposure data platform has provided valuable lessons learned and guided the operation and user support provided by CANUE. In this context, we discuss the data curation process, including data discovery and content selection when presented with multiple datasets. The lessons learned are aimed at data managers that must decide on the value of the data as well as adding value to the data through the curation process. Finally, we discuss some of the emerging challenges in building, sustaining, and future-proofing an environmental exposure data platform for urban health research.
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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 | 0.040 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".