Identifying micro-anatomical compartments of mammalian optic nerve based on NMR T/sub 2/-relaxation analysis
Bibliographic record
Abstract
We analyzed the differences in NMR transverse-relaxation (T/sub 2/) times for various nervous tissues. The unique characteristic of transverse relaxation in different micro-anatomical structures of nerve allows us to establish a relationship between the components of the T/sub 2/-spectrum and the micro-anatomical compartments of the nerve. Three different T/sub 2/ components have been identified in the frog and rat sciatic and rat optic nerves. Paramagnetic agents were used to demonstrate that optic nerve exhibits three different T/sub 2/-relaxation time components. For the frog and rat sciatic nerves, the short-lived component with a T/sub 2/ time of 10-20 ms is assumed to be a signal coming from myelin. The intermediate-lived component with a T/sub 2/ time of 80-100 ms is believed to originate from the interaxonal water and the long-lived component with a T/sub 2/ time of 240-260 ms is thought to be a signal originating from the axoplasm. For frog and rat sciatic nerves an accurate relationship between the components of the T/sub 2/ spectrum and the compartments of the micro-anatomical structures of the nerve has been established. However, for the rat optic nerve this relationship has not yet been identified.
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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 | 0.000 | 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".