Bibliographic record
Abstract
This book is about peripheral nerves, their unique biology and how they repair themselves during regeneration. The biology of the peripheral nervous system is not often considered on its own. Much has been learned about the neurosciences of peripheral nerves, specifically during injury and regeneration, but it is my sense that some of this new and exciting information should be consolidated and considered in an overview. Without nerves, specifically peripheral nerves, there is no movement, no sensation. Peripheral nerves are the essential connections between the body, brain, and spinal cord. The “peripheral nervous system (PNS)” distinguishes itself from the “central nervous system (CNS)” on many levels. Peripheral axons reside in many types of local environments including muscles, connective tissue, skin, and virtually every organ of the body. This reach extends into the meninges that surround the brain, a surprising fact to some. Moreover, peripheral neurons are very different from their CNS counterparts in how they respond to injury or disease, in which cells they partner with and in what axon trees they support. For example, a sensory neuron in the lumbar dorsal root sensory ganglion is required to maintain and support distal axon branches that can extend a meter or more to the skin of the toe. Only a small proportion of CNS neurons have comparable outreach and demands placed upon them. “Neuropathies,” of which there are a large number, are simply disorders of peripheral nerves.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.469 | 0.307 |
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".