Bibliographic record
Abstract
I t is an almost impossible task to describe Canadian contribu- tions to respiratory physiology and pathophysiology.There have been so many and they have been so important that it is difficult to pick and choose.Inevitably, there will be omissions; inevitably, some of us will not be pleased; and inevitably, one is biased by one's own experience.This will affect what I write, no matter how much I strive for objectivity.I apologize to anyone whose contributions I should have described but failed to do so.And to those whose work I do describe, I hope I have got it right.The focus of the present review is on the roots of respiratory physiological and pathophysiological research in Canada, and how this led to a contribution to the world literature far greater than what one might predict from our small population.While this legacy is still as strong today, only rarely do I refer to papers published in the past 25 years.The seeds planted in the late 1920s and early 1930s grew rapidly in the 1950s and came into full bloom in the 1960s and 1970s.The present story is about this period, and it is a story about individuals and places, as much as it is about the vast amount of new knowledge that was created.Fortunately, the flowers are perennials and the garden is more beautiful than ever, but that is another story.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 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; 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".