The Honorable Tony Clement, Minister of Health,
Bibliographic record
Abstract
I raise a serious concern about a document1 written by six scientists at Health Canada’s Consumer and Clinical Protection Bureau that was recently posted on the BC Centre for Disease Control2 web site. The Health Canada scientists purport to test the effectiveness of the Graham/Stetzer filters to reduce dirty electricity. This document does not appear on the Health Canada web site and has not been published in a peer-reviewed journal. Had it been peer reviewed it would not have been accepted for the obvious errors I mention below. This document is more concerned in protecting the electric utility than it is in protecting the health of Canadians. It surprises me that Health Canada would approve release of this document with so many fundamental errors. It is my understanding that this document has been circulated widely yet the Health Canada authors did not have the courtesy to send a copy of their report to the designers of this filter, Professor Martin Graham (UC Berkeley) and Mr. Dave Stetzer (President of Stetzer Electric). I ask you to look into this matter. Dave Stetzer has agreed to demonstrate how the filters work using appropriate equipment and I ask you to encourage your scientists at Health Canada to take him up on his offer. What follows is my evaluation of and response to the Health Canada document1. Sincerely,
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.040 | 0.016 |
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".