Integrating Environmental and Human Health Databases in the Great Lakes Basin: Themes, Challenges and Future Directions
Bibliographic record
Abstract
Many government, academic and research institutions collect environmental data that are relevant to understanding the relationship between environmental exposures and human health. Integrating these data with health outcome data presents new challenges that are important to consider to improve our effective use of environmental health information. Our objective was to identify the common themes related to the integration of environmental and health data, and suggest ways to address the challenges and make progress toward more effective use of data already collected, to further our understanding of environmental health associations in the Great Lakes region. Environmental and human health databases were identified and reviewed using literature searches and a series of one-on-one and group expert consultations. Databases identified were predominantly environmental stressors databases, with fewer found for health outcomes and human exposure. Nine themes or factors that impact integration were identified: data availability, accessibility, harmonization, stakeholder collaboration, policy and strategic alignment, resource adequacy, environmental health indicators, and data exchange networks. The use and cost effectiveness of data currently collected could be improved by strategic changes to data collection and access systems to provide better opportunities to identify and study environmental exposures that may impact human health.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".