The distribution, number, and certain neurochemical identities of infracortical white matter neurons in a lar gibbon (<scp><i>Hylobates lar</i></scp>) brain
Bibliographic record
Abstract
We examined the number, distribution, and immunoreactivity of the infracortical white matter neuronal population, also termed white matter interstitial cells (WMICs), in the brain of a lesser ape, the lar gibbon. Staining for neuronal nuclear marker (NeuN) revealed WMICs throughout the infracortical white matter, these cells being most numerous and dense close to cortical layer VI, decreasing significantly in density with depth in the white matter. Stereological analysis of NeuN-immunopositive cells revealed a global estimate of ~67.5 million WMICs within the infracortical white matter of the gibbon brain, indicating that the WMICs are a numerically significant population, ~2.5% of the total cortical gray matter neurons that would be estimated for a primate brain the mass of that of the lar gibbon. Immunostaining revealed subpopulations of WMICs containing neuronal nitric oxide synthase (nNOS, ~7 million in number, with both small and large soma volumes), calretinin (~8.6 million in number, all of similar soma volume), very few WMICs containing parvalbumin, and no calbindin-immunopositive neurons. These nNOS, calretinin, and parvalbumin immunopositive WMICs, presumably all inhibitory neurons, represent ~23.1% of the total WMIC population. As the white matter is affected in many cognitive conditions, such as schizophrenia, autism and also in neurodegenerative diseases, understanding these neurons across species is important for the translation of findings of neural dysfunction in animal models to humans. Furthermore, studies of WMICs in species such as apes provide a crucial phylogenetic context for understanding the evolution of these cell types in the human brain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".