A Collaborative Research Exploration of Pollutant Mixtures and Adverse Birth Outcomes by Using Innovative Spatial Data Mining Methods: The DoMiNO Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental health research is gaining interest due to the global concern of environmental factors impacting health. This research is often multifaceted and becomes complex when trying to understand the participation of multiple environmental variables. It requires the combination of innovative research methods, as well as the collaboration of diverse disciplines in the research process. The application of collaborative approaches is often challenging for interdisciplinary teams, and much can be learned from in-depth observation of such processes. We share here a case report describing initial observations and reflections on the collaborative research process of the Data Mining and Neonatal Outcomes (DoMiNO) project (2013–2018), which aimed to explore associations between mixtures of air pollutants and other environmental variables with adverse birth outcomes by using an innovative data mining approach. The project was built on interdisciplinary and user knowledge participation with embedded evaluation framework of its collaborative process. We describe the collaborative process, the benefits and challenges encountered, and provide insights from our experience. We identified that interdisciplinary research requires time and investment in building relationships, continuous learning, and engagement to build bridges between disciplines towards co-production, discovery, and knowledge translation. Learning from interdisciplinary collaborative research experiences can facilitate future research in the challenging field of environmental health.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it