Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microglia comprise a unique subset of glial cells as the principal brain immune cells and are actively engaged in physiological and pathological brain functions. Unlike other resident neural cells that are of neuroectodermal origin, microglia are of mesodermal origin and invade the neuroepithelium at early embryonic stages. As resident immune response cells, microglia are extremely sensitive to almost any brain disturbance. Therefore, microglia are traditionally recognized for their immune functions during acute brain injury, such as bacterial meningitis, ischemic stroke, and spinal cord injury, as well as chronic neurological disorders, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and neuropathic pain. Recently, the role of microglia in neurodevelopment and neural plasticity in the healthy brains has gained tremendous attention. These exciting results raise an intriguing possibility that microglia can integrate into the neuronal circuits in the healthy and diseased brain. In support of this notion, it is emerging that microglia have remarkably dynamic processes and are frequently interacting with neurons and synaptic elements. Through these interactions, microglia may monitor neuronal/synaptic activities and thus survey the microenvironment in the brain. Indeed, recent studies have apparently shown that microglia function in neuronal circuits by playing diverse roles in neural development, behavior, and pathology in the brain. Therefore, microglia research has changed the way we think about neuronal network/plasticity and increased our understanding of brain diseases associated with abnormal microglia. Contributions to this special issue provide a snapshot of microglial function in the healthy and diseased brain and propose a fundamental role of microglia in neuronal circuits.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it