Diversity in the oligodendrocyte lineage: Plasticity or heterogeneity?
Bibliographic record
Abstract
Heterogeneity is a widely recognized phenomenon within the majority of cell types in the body including cells of the central nervous system (CNS). The heterogeneity of neurons based on their distinct transmission modes and firing patterns has been recognized for decades, and is necessary to coordinate the immense variety of functions of the CNS. More recently, heterogeneity in glial cells has been identified, including heterogeneity in oligodendrocyte progenitor cells (OPCs) and oligodendrocytes. OPC subpopulations have been described based on their developmental origin, anatomical location in the grey or white matter, and expression of surface receptors. Oligodendrocytes are categorised according to differences in gene expression, myelinogenic potential, and axon specificity. Much of what is described as heterogeneity in oligodendrocyte lineage cells (OLCs) is based on phenotypic differences. However, without evidence for functional differences between putative subgroups of OLCs, distinguishing heterogeneity from plasticity and lineage state is difficult. Identifying functional differences between phenotypically distinct groups are therefore necessary for a deeper understanding of the role of OLCs in health and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".