Bibliographic record
Abstract
LEGACY LEGACYI still remember very vividly the first lecture that I ever heard from Prof. Peter Beighton.This was shortly after he arrived in Cape Town in 1972.Were it not for Peter, I do not think that I would ever have entered the field of human genetics.With his enthusiasm and brilliant presentation, and the capability of evoking excitement and dreams, Peter Beighton inspired me.Shortly after I had heard that lecture, I presented myself at the Department of Human Genetics, which was newly established and the first in Africa.I asked him whether I could participate in any research projects.I was particularly drawn to his research projects, as it appeared that they opened up new possibilities for understanding biology in different populations, which might have relevance for the general population.Peter's stories of his hiking through the Sahara Desert with the Tuareg, and his stories from different populations in Africa, inspired me and elucidated for me for the first time the power of studying rare families and their ability to inform general rules of biology.As a medical student, I then pursued many research projects with Prof. Beighton.I recall travelling with him to the Kalahari Desert in an effort to understand and explore the basis of steatopygia in the Khoisan people.I also recall very vividly the first introduction that Peter made for me to patients with increased bone density.Peter was an expert and had already specialised in patients with bone dysplasias, including increased bone density, and had authored authoritative manuscripts on sclerosing bone dyplasias.[1,2] I was fortunate to go with Peter to visit families with sclerosteosis in different parts of the country.These were very moving moments as I saw, first hand, the impact of this increased bone density on patients.[3,4] In a few instances, we actually saw, to our deep sadness, the increased intracranial pressure leading to severe symptoms and occasionally also to death, as a result of the increased density of the skull.These vivid images have stayed with me throughout my life and have also inspired me to look and see what we could learn from and do for those with rare conditions.After I moved to Vancouver, to the University of British Columbia from Harvard Medical School in 1983, and founded my first company, Xenon Genetics, I remembered those patients with sclerosteosis and went back to Peter.I persuaded him and others to send a postdoctoral fellow to the USA in an effort to bring DNA to clone that particular gene.When I looked at the X-rays of these patients with Peter, it was obvious that this was not a disorder of osteoclast overactivity, but appeared to be most likely due to unregulated overexpression of the osteoblast.I
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.037 | 0.031 |
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".