Using Plethysmography to Determine Erythropoietin’s Impact on Neural Control of Ventilation
Bibliographic record
Abstract
The evaluation of respiratory parameters often requires the use of anesthetics (that depress the neural -network controlling respiration), and/or ways to restrain the animal's mobility (that produces a stress-dependent increase of respiration). Consequently, the establishment of plethysmography represented an invaluable technique in respiratory physiology. Plethysmography, indeed, allows the assessment of ventilatory parameters on living, unanesthetized, and unrestrained animals. The conception of the barometric plethysmography relies on the fact that an animal placed inside a hermetically closed chamber generates through its breathing a fluctuation of pressure in the chamber than can be recorded. Thus, the respiratory frequency and the tidal volume can be directly measured, while the animal's ventilation is calculated indirectly by the multiplication of these two parameters. In our hands, plethysmography was a key tool to investigate the impact of erythropoietin (Epo) on the neural control of hypoxic ventilation in mice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".