Marion David Francis: 1923–2016
Bibliographic record
Abstract
“Dave” Francis, our cherished friend and colleague, died on May 10, 2016, a day after his 93rd birthday. He made major scientific discoveries in the dental and medical fields that improved the lives of millions of people around the world. Dave was born in Vancouver, Canada, gained his Bachelor's degree in physics and chemistry at the University of British Columbia (UBC) followed by a Master's degree in analytical chemistry at UBC and a doctorate in biophysical chemistry at the University of Iowa in 1951. In 1952, he joined the Procter and Gamble Company (P&G) at the Miami Valley Laboratories in Cincinnati, OH, USA, where he spent most of his professional career, publishing over 100 papers and 35 patents. Dave's early work was in the area of dental research. His basic research on the chemistry of tooth enamel structure and formation and of the mechanism by which fluoride ions interfere with the destruction of tooth enamel and dentine helped to lay the scientific foundation for the effective use of fluoride to prevent caries.(1) The widespread adoption of fluoride therapies resulted in a more than 70% reduction in the incidence of dental caries among children from 1967 to 1988. As a testimony to the significance of this work, P&G received special recognition from the American Dental Association for the prevention of dental caries. P&G's Crest® and many other fluoride‐containing toothpastes continue to be in use more than 50 years later.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.180 | 0.157 |
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".