Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Magnetic resonance imaging has become an important noninvasive technique to gain insight into fetal brain development. Its capabilities go beyond ultrasound when diagnosing high-risk pregnancies. To summarize observations across a population in magnetic resonance imaging studies, reference systems such as atlases that establish correspondences across a cohort are key. In this article, we review the evolution of atlas-building methods in light of their relevance, limitations, and benefits for the modeling of human brain development. Starting with single anatomical templates to which brain scans where mapped to such as Talairach and Montreal Neurological Institute space, we explore the uses of atlases as a means to establish correspondences across a cohort and as a model that captures the population characteristics of the cases the atlas is built from. We discuss methods that capture features of increasingly heterogeneous populations and approaches that are able to generalize with only minimal annotation. The main focus of this review are methods that explicitly model the variability in the population with regard to time, such as in the modeling of disease progression and brain development. We highlight the applicability and limitations of state-of-the art approaches, how insights from the study of disease progression are helpful in developmental studies, and point to the directions of future research that is still necessary.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it