What are the Causes, Consequences and Correctives of fish contamination in the Detroit River AOC that cause health consumption advisories? (Final Report, Michigan Sea Grant MICHU-T-10-001)
Bibliographic record
Abstract
Using an Integrated Assessment (IA) framework, we consolidated and utilized existing data from the Detroit River to develop models that both identify possible drivers of elevated fish contaminant body burdens, and to facilitate a more risk-based approach of tissue trigger-levels for consumption advisories. This integrated assessment approach was particularly useful for consumption advisories as it provided the ability to integrate and organize complex data in a manner that can help inform management decisions. In addition, the IA framework explicitly fosters collaboration and participation of multiple interested groups. We have capitalized on this component of IAs, by seeking the active participation of different stakeholder groups in developing logic models that identify the goals of the scientific assessment and the connections between the science and management or policy outcomes. This science-policy connection was the focus of workshops designed to evaluate frameworks identifying the goals and desired outcomes from the scientific assessment component of the IA. We approached this IA with the following objectives: 1) Synthesize and summarize the status and trends of fish contaminant levels and advisories in the Detroit River, through an assessment of the trends in the data as well as documentation of the history of the fish advisory in the river Canadian and U.S. 2) Describe the abiotic, biotic, and human health causes and consequences of fish consumption advisories, with an emphasison model simulation and explanatory analyses. This effort will focus on the environmental conditions that contribute to fish consumption advisories in addition to other factors that may exacerbate human health risks. 3) Identify the key uncertainties regarding the drivers of consumption advisories for use in prioritizing future research and monitoring efforts and in helping guide management and policy directives. 4) Utilize information on the causes and consequences of consumption advisories for providing technical guidance in implementing policy and management options. This will include a focus on short-term measures that reduce direct threats to human health and longer-term objectives to reduce overall body burden of fish in the Detroit River relative to reference areas.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".