Spinal Cord Trauma: Regeneration, Neural Repair and Functional Recovery
Bibliographic record
Abstract
This book, volume 137 of Progress in Brain Research, contains thirty-four chapters that summarize the platform presentations at the XXIII International Symposium of the Center for Research in Neurological Sciences of the University of Montreal, held in May 2001. Most of the contributors are internationally renowned investigators in the field of spinal cord injury research. Topics covered include imaging, rehabilitation, and electrical stimulation in human subjects; specific aspects (such as cell death, grey matter repair, mechanisms of autonomic dysreflexia, and robotics) of different animal models of spinal cord injury; strategies for repair and neuroprotection; and molecular targets (such as extracellular matrix components, Nogo and the Nogo-66 receptor, the Rho family of GTPases, and the immune system) for promoting axonal regeneration. This book is neither comprehensive nor thematically focused. It is rather a compendium of widely disparate topics, most of which will be of interest to some but not to others. The majority of the chapters are brief summaries of ongoing work in specific laboratories. Chapters vary in length from six to twenty-five pages, and reference lists run from less than half a page to twelve pages. Some have abstracts and others do not. The illustrations are generally of high quality.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".