Bibliographic record
Abstract
Structure is a key determinant of function, with the nervous system being no exception. For example, in the nervous system the physiological properties of different synapses may be understood by comparing their structures. However, it is not clear whether specific structural properties of some neurons might play a role in driving their selective removal during chronic neurodegeneration or whether the structural properties might underpin why particular types of synapses or other neuronal compartments are more susceptible to degeneration (i.e., become dysfunctional) in certain brain regions than in others. Our recent study of the ultrastructure of the hippocampus and the cerebellum revealed that early synaptic loss is not a ubiquitous event in a brain undergoing chronic neurodegeneration. The prominent structural differences in proximity of the synaptic environment that are brought about by a degree of synaptic ensheathment by glial cells may help explain why Purkinje cell synapses remain intact, while pyramidal cell synapses progressively degenerate. The intrinsic structural organization of the hippocampal neuropil could contribute to the susceptibility of synapses to extracellular protein misfolding by a relatively higher degree of synaptic exposure to the extracellular environment. We suggest that neuronal structure may determine more than function; it might also predict dysfunction.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".