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Record W7097191441

London Ontario Canada CIVIC WORKS COMMITTEE MEETING ON JANUARY 25, 2012 DRINKING WATER FLUORIDATION IN LONDON

2012· article· en· W7097191441 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsnot available
Fundersnot available
KeywordsHarmGovernment (linguistics)Water fluoridationScientific evidenceGovernment regulationScientific consensusResistance (ecology)Public health
DOInot available

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.006
GPT teacher head0.187
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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