GLOBAL COUNCIL ON BRAIN HEALTH: ADVANCING INTERNATIONAL DIALOGUE TO PROMOTE WELL-BEING
Bibliographic record
Abstract
As people live longer, the need for clear, trustworthy information on brain and cognitive health is greater than ever. Launched in 2015, the Global Council on Brain Health is an independent collaborative of scientists, clinicians, scholars and policy experts convened by AARP to provide the foremost thinking on what people and professionals can do to maintain and improve brain health. The goal of the Council is to translate scientific research into actionable recommendations for the public that will help drive behavior change in individuals across communities and cultures. This symposium will feature leading researchers from the UK, US and Canada to highlight recommendations issued by the Council. It will showcase three consensus documents generated by the Council that are based on the latest research advancements. Particular emphasis will be placed on Council recommendations that are aimed at improving brain health in three areas: physical exercise, sleep and social engagement. In sum, this symposium brings together leaders at the forefront of this international effort to discuss the scientific and policy dimensions of brain health.
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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 | 0.093 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.044 | 0.052 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".