From ‘Nerve Fiber Regeneration’ to ‘Functional Changes’ in the Human Brain—On the Paradigm-Shifting Work of the Experimental Physiologist Albrecht Bethe (1872–1954) in Frankfurt am Main
Bibliographic record
Abstract
Until the beginning 1930's the traditional dogma that the human central nervous system (CNS) did not possess any abilities to adapt functionally to degenerative processes and external injuries loomed large in the field of the brain sciences (Hirnforschung). Cutting-edge neuroanatomists, such as the luminary Wilhelm Waldeyer (1836-1921) in Germany or the Nobel Prize laureate Santiago Ramón y Cajal (1852-1934) in Spain, debated any regenerative and thus "plastic" properties in the human brain. A renewed interest arose in the scientific community to investigate the pathologies and the healing processes in the human CNS after the return of the high number of brain injured war veterans from the fronts during and after the First World War (1914-1918). A leading research center in this area was the "Institute for the Scientific Study of the Effects of Brain Injuries," which the neurologist Ludwig Edinger (1855-1918) had founded shortly before the war. This article specifically deals with the physiological research on nerve fiber plasticity by Albrecht Bethe (1872-1954) at the respective institute of the University of Frankfurt am Main. Bethe conducted here his paradigmatic experimental studies on the pathophysiological and clinical phenomena of peripheral and CNS regeneration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.023 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".