Bibliographic record
Abstract
Population neuroscience endeavours to identify environmental and genetic factors that shape the function and structure of the human brain; it uses tools and knowledge of genetics, epidemiology, and cognitive neuroscience. Here, I focus on the application of population neuroscience in studies of brain development. By describing in some detail four existing large-scale magnetic resonance (MR) imaging studies of typically developing children and adolescents, I provide an overview of their design, including population sampling and recruitment, assessments of environmental and genetic "exposures," and measurements of brain and behavior "outcomes." I then discuss challenges faced by investigators carrying out such MR-based studies, including quality assurance, quality control and intersite coordination, and provide a brief overview of the achievements made so far. I conclude by outlining future directions vis-à-vis population neuroscience, such as design strategies that can be used to evaluate the presence of absence of causality in associations discovered by observational studies.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".