Public Awareness of Mercury in Fish: Analysis of Public Awareness and Assessment of Fish Consumption in Vermont
Bibliographic record
Abstract
Exposure to mercury from environmental sources, such as fish consumption, poses potential health risks to the public. The state of Vermont has developed educational brochures and posters displaying safe fish consumption guidelines in order to educate the public regarding mercury exposure through fish. In this study, a group of medical students from the University of Vermont College of Medicine, in partnership with the Vermont Department of Health, conducted a study in Chittenden County, Vermont in order to assess both fish consumption practices and overall awareness of such safe eating guidelines and mercury advisories. A total of 166 Vermont residents were surveyed during a six week period. The results of this survey suggest that in Chittenden county of Vermont, these educational efforts are markedly successful, with 48% of respondents being specifically aware of the safe eating guidelines. Further, these results suggest that 61% of those respondents that reported low monthly canned tuna consumption had a decreased their consumption in response to the safe eating guidelines. last, a series of specific, yet widely applicable recommendations are presented for future public educational efforts regarding mercury exposure through fish consumption.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".