London Ontario Canada CIVIC WORKS COMMITTEE MEETING ON JANUARY 25, 2012 DRINKING WATER FLUORIDATION IN LONDON
Bibliographic record
Abstract
Drinking water fluoridation has been implicated as a cause of gene damage leading to birth defects and cancer. Government regulators evaluate scientific studies based one of two regulatory philosophies. One philosophy demands absolute and unequivocal evidence of harm ( a body count) while the other approach is called the precautionary principle which argues that some evidence of harm should cause stoppage of the use of an offending drug or environmental pollutant until the item is proven safe by further experiments. In Europe the precautionary principle is written into law while in Canada and the United States regulators depend on elevated body counts to act on a particular cause of harm. There is a current and growing body of peer reviewed scientific publications showing that fluoridated water causes gene damage leading to birth defects and cancer and that humans are genetically different in their sensitivity to levels of fluoride in their drinking water. Some current peer reviewed scientific publications that have not yet been chewed over by the proponents of drinking water fluoridation are described below. These studies should have been sufficient to trigger eliminate of the addition of fluoride to the drinking water supply. Resistance to fluoride toxicity: People are not genetically uniform clones they are highly polymorphic in their response to drugs and environmental toxins. According to a recent report
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.342 | 0.078 |
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".