Bibliographic record
Abstract
The contributions of Canadians to pulmonary anatomy and pathology have been recognized internationally for almost a century, and the published abstracts of the 2007 meetings of the American Thoracic Society indicate that Canada has a bright future in this field. The introduction of computed tomography (CT) has had the greatest impact on the practice of chest medicine within living memory, because it allows the gross anatomy of the lung to be visualized noninvasively. The more recent introduction of micro-CT has also provided an opportunity to investigate samples of the lung at the microscopic level without destroying the tissue. Micro-CT has made it possible to apply the newer techniques of laser capture microdissection and real-time polymerase chain reaction to specific lung structures selected by CT and micro-CT, and to ask questions that could not even have been imagined a few short years ago. The introduction of magnetic resonance imaging of hyperpolarized gases has also made it possible to measure diffusion distances within the gas phase of the lung to obtain new and perhaps more accurate information about the size of normal gas-exchanging units during life, as well as the extent of their destruction in disease. These powerful tools are beginning to provide insight into the molecular events that underlie the basic processes of inflammation, tissue repair and tumour biology, which may provide solutions to the most troublesome problems related to pulmonary medicine. Although many Canadians have and are currently contributing to the flow of this new knowledge, the present review focuses on contributions made by three eminent researchers, all of whom who have been dead for at least 10 years. These three researchers established a tradition of excellence in the field of pulmonary anatomy and pathology in Canada that has provided a solid foundation for future achievements.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".