The Food and Drug Administration Agrees to Classify Mercury Fillings
Bibliographic record
Abstract
In the United States Court of Appeals of the District of Columbia Circuit, the Appellants Mom's Against Mercury, Connecticut Coalition for Environmental Justice, Oregonians for Life, California Citizens for Health Freedom, Kevin J. Biggers, Karen Johnson, Linda Brocato, R. Andrew Landerman, and Antia Vazquez Tibaul filed a petition for review of Regulatory Inaction by the Food and Drug Administration (FDA). On Monday June 2, 2008, the lawsuit was settled with the FDA after it agreed to classify mercury fillings. During its negotiation session with the Appellants, the FDA indicated that it would change its website on mercury fillings. The FDA no longer claims that no science exists about the safety of mercury amalgam or that other countries have acted for environmental reasons only. On its website, the FDA now states the following: "Dental amalgams contain mercury, which may have neurotoxic effects on the nervous systems of developing children and fetus." The FDA also states that "Pregnant women and persons who may have a health condition that makes them more sensitive to mercury exposure, including individuals with existing high levels of mercury bioburden, should not avoid seeking dental care, but should discuss options with their health practitioner." The FDA decision to classify mercury fillings is a reflection of the legislations enacted in Europe and Canada that highlight the neurotoxic effects of mercury fillings.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.027 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".