Bibliographic record
Abstract
INTRODUCTION Normative data, reflecting normal values of the healthy population, provides valuable information for clinical research. Depending on the specific region of interest, normative data can be used as a comparison when studying specific diseases. The purpose of this project is to build a database of quantitative magnetic resonance (MR) measurements of the brain in the healthy population. By doing so, this dataset can serve as control groups for studies of various neurological disorders. Once the normative data is collected, it is also important to understand the reliability and variability of the results obtained. METHODS The objective is to recruit 120 healthy participants between the ages of 18-89 (20 participants per age decade; 10 males and 10 females) to participate in the study. Exclusion criteria for the study include: a) history of neurological disorder, b) MR incompatible, c) claustrophobia, d) composite score below 27 on the Montreal Cognitive Assessment (MoCA). The MoCA is a sensitive tool for detecting mild cognitive impairment, which is administered by a qualified trainee during the study. A series of quantitative MR measurements of the brain are acquire
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".