Bibliographic record
Abstract
Abstract The main functions of the respiratory neural network are to produce a coordinated, efficient, rhythmic motor behavior and maintain homeostatic control over blood oxygen and CO 2 /pH levels. Purinergic (ATP) signaling features prominently in these homeostatic reflexes. The signaling actions of ATP are produced through its binding to a diversity of ionotropic P2X and metabotropic P2Y receptors. However, its net effect on neuronal and network excitability is determined by the interaction between the three limbs of a complex system comprising the signaling actions of ATP at P2Rs, the distribution of multiple ectonucleotidases that differentially metabolize ATP into ADP, AMP, and adenosine (ADO), and the signaling actions of ATP metabolites, especially ADP at P2YRs and ADO at P1Rs. Understanding the significance of purinergic signaling is further complicated by the fact that neurons, glia, and the vasculature differentially express P2 and P1Rs, and that both neurons and glia release ATP. This article reviews at cellular, synaptic, and network levels, current understanding and emerging concepts about the diverse roles played by this three‐part signaling system in: mediating the chemosensitivity of respiratory networks to hypoxia and CO 2 /pH; modulating the activity of rhythm generating networks and inspiratory motoneurons, and; controlling blood flow through the cerebral vasculature. © 2013 American Physiological Society. Compr Physiol 3:331‐363, 2013.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".