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 participate in all phases of the multiple sclerosis (MS) disease process. As members of the innate immune system, these cells have evolved to respond to stranger/danger signals; such a response within the central nervous system (CNS) environment has the potential to induce an acute inflammatory response. Engagement of Toll-like receptors (TLRs), a major family of pattern-recognition receptors (PRRs), provides an important mechanism whereby microglia can interact with both exogenous and endogenous ligands within the CNS. Such interactions modulate the capacity of microglia to present antigens to cells of the adaptive immune system and thus contribute to the initiation and propagation of the more sophisticated antigen-directed responses. This inflammatory response introduces the potential for bidirectional feedback between CNS resident and infiltrating systemic cells. Such interactions acquire particular relevance in the era of therapeutics for MS because the infiltrating cells can be subjected to systemic immunomodulatory therapies known to change their functional properties. Phagocytosis by microglia/macrophages is a hallmark of the MS lesion; however, the extent of tissue damage and the type of cell death will dictate subsequent innate responses. Microglia/macrophages are armed with a battery of effector molecules, such as reactive nitrogen species, that may contribute to CNS tissue injury, specifically to the injury of oligodendrocytes that is associated with MS. A therapeutic challenge is to modulate the dynamic properties of microglia/macrophages so as to limit potentially damaging innate responses, to protect the CNS from injury, and to promote local recovery.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it