Neural transplantation
Bibliographic record
Abstract
Introduction In spite of early attempts at neural transplantation as long ago as the late nineteenth century, throughout most of the twentieth century it was widely believed that the mammalian brain was relatively fixed and immutable in adulthood, incompatible with receiving and supporting viable transplants. However, at the end of the 1960s, two discoveries challenged this received view: the demonstration that sprouting and reorganization of axons can indeed take place after damage in adult central nervous system (CNS) pathways; and new experimental methods for transplanting nerve cells that were remarkably successful in yielding surviving grafts. In the first decade after these pioneering studies, attention focused on understanding the basic cellular and developmental biology of neural transplantation in a variety of model systems. Cells were transplanted into the CNS of adult rats using a wide variety of experimental model systems – anterior eye chamber, spinal cord, cerebellum, and diverse forebrain sites including cortex, hypothalamus, striatum, and hippocampus. In the first wave of studies (as illustrated in Fig. 17.1), pieces of neural tissue were implanted into natural cavities such as the anterior chamber of the eye, the brain ventricles or choroidal fissure. In the search for a greater flexibility of graft placement, other studies introduced inoculation of tissue fragments directly into brain parenchyma, although such grafts did not survive well, or the creation of artificial cavities with a rich vascular lining that would nourish newly grafted tissues.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.028 |
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".