Bibliographic record
Abstract
Abstract Functional magnetic resonance imaging (FMRI) is an analytical method for measuring brain activity while it occurs. FMRI was first demonstrated in 1992, but it has since become the most popular neuroimaging method. Its temporal resolution is of the order of seconds and hence superior to positron emission tomography (PET). Its spatial resolution is on the order of millimeters which makes it superior to both PET and electrophysiological methods such as electroencephalography (EEG). Furthermore, FMRI is noninvasive in the sense that no external contrast agent has to be used. FMRI contrast is based on the intrinisic blood oxygenation changes that occur at the site of brain activity in response to a specific task. The exact mechanism that links activity and signal change is currently not well understood and is an area of active research. FMRI is subject to many experimental difficulties, however. A vexing problem is that of physiological (heartbeat and breathing) and gross motion. Gross motion is often coupled to the presentation of the stimulus and hence especially prone to producing artefactual activation. The analysis of the experimental data is not a standard procedure at present. While past research has generally used paradigmatic methods of analysis (hypothesis testing), nonparadigmatic (data driven) methods like fuzzy clustering analysis (FCA) or independent component analysis (ICA) have become important tools. A more complete understanding of the physiological mechanisms leading to the activation signal, and a better grasp of the proper statistical treatment of the data, are likely to increase the power of FMRI even further.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".