Bibliographic record
Abstract
Review (attached from fluoridation.com), Canadian Dental Association Position Paper; and, my request to have fluoridation removed without plebiscite and without further Review; Dear Mayor Nenshi and Councilors I have attached my submission to the Jan 26th Council Committee which heard presentations from the public about water fluoridation. I am respectfully requesting that Council remove fluoride from our public water supply without plebiscite and without further Review. More than one Councilor questioned fluoridation proponents about the daily dose of fluoride that a person receives versus the concentration of fluoride in water. Every single one of them danced around those pointed questions by Committee members. This is a common obfuscation by fluoridation proponents because they would have to admit that many people receive far too much daily fluoride and are at risk of negative health effects while others receive little or nothing. Even Dr Keagan from the U of C declined to answer the question of whether he as a medical professional was okay with some people receiving more fluoride than the so-called "average". Based on my personal water consumption of 3-4 liters of water a day on the days I go to the gym and train, and my daily coffee intake of 4-6 cups per day (1.5 liters), if I were drinking
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.815 | 0.735 |
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".