A primer for brain imaging: a tool for evidence-based studies of nutrition?
Bibliographic record
Abstract
Nutrition affects brain structure and function throughout life. Nutritional scientists and practitioners are interested in gathering evidence clarifying which of the micro- or macronutrients may affect particular aspects of brain functioning at a given period of the life cycle and in identifying possible brain mechanisms underlying such effects. This article provides a primer on brain imaging techniques suitable for the assessment of the structure and function of the human brain, focusing on noninvasive techniques such as structural and functional magnetic resonance imaging, electroencephalography, and magnetoencephalography. The article concludes with a few suggestions regarding the choice of a particular imaging tool in observational studies and randomized controlled trials investigating nutritional effects on the human 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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".