Neural Compensations After Lesion of the Cerebral Cortex
Bibliographic record
Abstract
Functional improvement after cortical injury can be stimulated by various factors including experience, psychomotor stimulants, gonadal hormones, and neurotrophic factors. The timing of the administration of these factors may be critical, however. For example, factors such as gonadal hormones, nerve growth factor, or psychomotor stimulants may act to either enhance or retard recovery, depending upon the timing of administration. Nicotine, for instance, stimulates recovery if given after an injury but is without neuroprotective effect and may actually retard recovery if it is given only preinjury. A related timing problem concerns the interaction of different treatments. For example, behavioral therapies may act, in part, via their action in stimulating the endogenous production of trophic factors. Thus, combining behavioral therapies with pharmacological administration of compounds to increase the availability of trophic factors enhances functional outcome. Finally, anatomical evidence suggests that the mechanism of action of many treatments is through changes in dendritic arborization, which presumably reflects changes in synaptic organization. Factors that enhance dendritic change stimulate functional compensation, whereas factors that retard or block dendritic change block or retard compensation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".