Bibliographic record
Abstract
When the twentieth century began, a substantial amount was already known about the mechanics of breathing, and the respiratory muscles. The chest wall had been modeled as a bellows with a bladder inside representing the lung. It was known that the respiratory system was elastic and stored energy on inflation that did the work of expiration. Measurements of transpulmonary pressure had been made and there had been attempts to correlate these with lung volume (1). However, a century ago we had no framework in which to view measurements. Without a model of how the system behaves, measurements of respiratory mechanics are uninterpretable. In addition, we lacked technology to make measurements. In the past 100 years this has changed: modeling has provided great insights into the system’s behavior and technological advances have permitted measurements that previously were impossible. Sometimes, however, advances are made without new models and new technology because scientists view problems from new perspectives. Such appears to be the case with surface tension, which I discuss separately. This review is not comprehensive, but focuses on the role of models and technology in the development of new knowledge in the field and on discoveries related to surface tension. Only a limited number of topics are discussed. I apologize to those whose work should be included, but that I fail to discuss. MODELING Lung Models
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".