Proceedings from the Albert Charitable Trust Inaugural Workshop on ‘Understanding the Acute Effects of Exercise on the Brain’
Bibliographic record
Abstract
An inaugural workshop supported by "The Leo and Anne Albert Charitable Trust," was held October 4-7, 2019 in Scottsdale, Arizona, to focus on the effects of exercise on the brain and to discuss how physical activity may prevent or delay the onset of aging-related neurodegenerative conditions. The Scientific Program Committee (led by Dr. Jeff Burns) assembled translational, clinical, and basic scientists who research various aspects of the effects of exercise on the body and brain, with the overall goal of gaining a better understanding as to how to delay or prevent neurodegenerative diseases. In particular, research topics included the links between cardiorespiratory fitness, the cerebrovasculature, energy metabolism, peripheral organs, and cognitive function, which are all highly relevant to understanding the effects of acute and chronic exercise on the brain. The Albert Trust workshop participants addressed these and related topics, as well as how other lifestyle interventions, such as diet, affect age-related cognitive decline associated with Alzheimer's and other neurodegenerative diseases. This report provides a synopsis of the presentations and discussions by the participants, and a delineation of the next steps towards advancing our understanding of the effects of exercise on the aging brain.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".