Bibliographic record
Abstract
Abstract This interactive resource is composed of three self-learning modules that will allow undergraduate health science and medical students to explore the anatomy and physiology of respiration. The idea for this resource arose from gaps in student understanding identified from short-answer questions that asked students to apply their knowledge of the regulation of lung ventilation in a real-life scenario. In these modules, students will construct the airflow pathway into and out of the lungs and justify the role of gradients in driving the exchange of respiratory gases between the alveolar air and the bloodstream. Through investigation of the regulation of respiration they will recognize the key role played by blood carbon dioxide levels in governing lung ventilation. They will then apply their new knowledge through real life and clinical scenarios that will permit them to recognize situations in which fluctuations in blood carbon dioxide levels will stimulate appropriate changes in lung ventilation in an effort to maintain homeostasis. Dead space volume, a particularly challenging concept for students, will be the focus of some of the clinical and daily life applications. Throughout this series of modules there will be ample opportunities for self-testing with the provision of feedback to guide learning and promote understanding of the physiological regulation of respiration.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.497 | 0.177 |
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".