Bibliographic record
Abstract
Abstract The growing field of neuroimaging has offered exciting insights into the inner workings of the human brain in health and disease. Structural neuroimaging techniques provide detailed information about the physical properties and anatomy of the brain and nervous system, including cerebrospinal fluid, blood vessels, and different types of tissue. The most commonly used structural neuroimaging techniques are computed tomography (CT) and structural magnetic resonance imaging (MRI). CT uses X-rays to create a two-dimensional representation of neural tissue, whereas MRI quantifies differences in tissue density by manipulating molecules using magnetic fields, magnetic field gradients, and radio waves. Functional neuroimaging techniques provide a measure of when and where activity is occurring in the brain by quantifying underlying physiological processes. Functional neuroimaging techniques fall into two broad categories: measures of direct brain activity, including electroencephalography (EEG) and magnetoencephalography (MEG), and measures of indirect brain activity, such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). Different functional neuroimaging techniques can be used to examine different physiological changes, including electrical activity, magnetic field changes, metabolic and neurotransmitter activity, and indirect measures of blood flow to offer insight into cognitive processing. Structural and functional neuroimaging have made a profound impact on understanding the brain both during normal functioning and in clinical pathology. Overall, neuroimaging is a powerful tool for both research and clinical practice and offers a noninvasive window into the central nervous system of humans in both health and disease.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.192 | 0.081 |
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".