Bibliographic record
Abstract
I enjoyed reading in the CMAJ about Dr. Blum’s encounter with Dr. Wilder Penfield.1 I recall first seeing Dr. Pen-field in the cafeteria at the Royal Victo-ria Hospital when I was a medical stu-dent (probably around 1970) and being somewhat awed by his presence. I had read The Second Career a few years before, and I also knew him by reputa-tion, through the media. The memory that stands out most vividly is of attending grand rounds in the amphitheatre of the Montreal Neu-rological Institute sometime during 1975–1976, when I was a final-year radiology resident. I arrived late, just as the “patient, ” an elderly man with a pro-tuberant abdomen distending his dress-ing gown, got up from his wheelchair and began to describe the histology slides of his own untreatable abdominal sarcoma. When I realized that the patient was Dr. Penfield, the enormous-ness of the moment struck me. I admire Dr. Blum’s maturity and self-confidence as an intern, in having been able to sit down and carry out a personal conversation with Dr. Pen-field. Only after several years of med-ical practice did I reach the point where I became at ease when dealing with famous people and was able to see the common humanity that we all share.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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; both teacher heads agree on what is shown here.
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".