Bibliographic record
Abstract
In this opening section of the Aging Brain we set the stage for the contributions that follow by providing a broad overview of the latest advances in our understanding of how the brain changes, both structurally and functionally, across the adult lifespan. We leave domain-specific aspects of brain aging to the subsequent chapters, where contributors will provide more targeted accounts of brain change germane to their particular focus on the aging brain. Here we review the extant, and rapidly expanding literature to provide a brief overview and introduction to structural and functional change that occur with typical brain aging. We begin the chapter by looking back, to review some of the early discoveries about how the brain changes across the adult lifespan. We close the chapter by looking forward, towards new discoveries that challenge our core assumptions about the inevitability or irreversibility of age-related brain changes. These sections serve as bookends for the core of the chapter where we review the latest research advances that continue to uncover the mysteries of the aging brain. Spreng, R.N., Turner, G.R. (2019, forthcoming) Structure and function of the aging brain. In G Samanez-Larkin (Ed.) The aging brain. Washington DC: American Psychological Association.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".