Capacity building in stakeholders around Detroit River fish consumption advisory issues
Bibliographic record
Abstract
The Detroit River is an international water body that has several fish consumption advisories for contaminants that affect human health and economic revenue for the USA and Canada. Despite the importance of these advisories, little progress has been made in developing effective management strategies or coordinating monitoring, research, and policy efforts between the 2 nations. We engaged 44 stakeholder organizations to increase community capacity on these issues for the Detroit River. We assessed capacity with key informant interviews and a network survey. Our analysis identified weak ties in information sharing and collaboration between countries. We used this information to improve stakeholder capacity, which included forming working groups that focused on system analysis, identification of priority issues, and definitions of organizational roles. Outcomes included outreach materials addressing environmental-justice issues and risk-analysis models of polychlorinated biphenyl (PCB) body burdens in fish. Our assessment of workshop participants with a longitudinal survey indicated that we increased network capacity and issue awareness in our stakeholders by providing new ways for them to work together. The engagement of stakeholders also improved research outcomes. By identifying stakeholder concerns related to scientific questions about consumption advisories early in the process, researchers were able to direct their efforts to generating translational research that better addressed stakeholder needs.
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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 | 0.052 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".