Bibliographic record
Abstract
Astrocytes play essential roles in nearly all aspects of brain function from modulating synapses and neurovasculature to preserving appropriate extracellular solute concentrations. To meet the complex needs of the central nervous system, astrocytes possess highly specialized properties that are optimized for their surrounding neural circuitry. Precisely how these diverse astrocytes types are generated in vivo, however, remains poorly understood. Key to this process is a critical balance of intrinsic developmental patterning and context-dependent environmental signaling events that configures astrocyte phenotype. Indeed, emerging lines of evidence indicate that persistent cues from neighboring cells in the mature CNS cooperate with early patterning events to promote astrocyte diversity. Consistent with this, manipulating Sonic hedgehog (Shh), Notch, and fibroblast growth factor (FGF) signaling in the adult brain, have profound effects on the structural, morphological, and physiological state of mature astrocytes. These pathways may become disrupted in various neurological diseases and contribute to CNS pathology. This mini-review focuses on how context-dependent environmental cues cooperate with intrinsic developmental patterning events to control astrocyte diversity in vivo in order to promote healthy brain function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".