Advancing Lung Immunology Research: An Official American Thoracic Society Workshop Report
Bibliographic record
Abstract
The mammalian airways and lungs are exposed to a myriad of inhaled particulate matter, allergens, and pathogens. The immune system plays an essential role in protecting the host from respiratory pathogens, but a dysregulated immune response during respiratory infection can impair pathogen clearance and lead to immunopathology. Furthermore, inappropriate immunity to inhaled antigens can lead to pulmonary diseases. A complex network of epithelial, neural, stromal, and immune cells has evolved to sense and respond to inhaled antigens, including the decision to promote tolerance versus a rapid, robust, and targeted immune response. Although there has been great progress in understanding the mechanisms governing immunity to respiratory pathogens and aeroantigens, we are only beginning to develop an integrated understanding of the cellular networks governing tissue immunity within the lungs and how it changes after inflammation and over the human life course. An integrated model of airway and lung immunity will be necessary to improve mucosal vaccine design as well as prevent and treat acute and chronic inflammatory pulmonary diseases. Given the importance of immunology in pulmonary research, the American Thoracic Society convened a working group to highlight central areas of investigation to advance the science of lung immunology and improve human health.
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 | 0.021 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.043 |
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".