The Canadian Urban Environmental Health Research Consortium (CANUE) - Enabling Collaborative Multi-Factor Environmental Health Research
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
In 2015, the Canadian Institutes of Health Research (CIHR) called for a new national consortium that would bring together scientific and other expertise from a wide variety of disciplines and fields from academia, government, charities and industry, to focus on specific research priorities that can only be addressed through interdisciplinary and intersectoral research. This included developing a 'data and methodological hub' where environmental researchers could collaborate with cohorts and health researchers on focused health projects using innovative measurement models and 'analysis-ready' data. Responding to this call, the Canadian Urban Environmental Health Research Consortium (CANUE) was established and aims, through a coordinated program, to capitalize on Canada’s growing big data capacity by facilitating the linkage of extensive geospatial exposure data to the wealth of established cohorts and administrative health data holdings (http://canue.ca). This presentation will provide an overview of CANUE’s vision, structure, and related strategic plan, with a particular focus on successes and challenges during our first two years of operation and opportunities to advance environmental health research towards the goal of healthier urban populations.
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.
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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