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
Abstract A good understanding of the 3D organization of deep brain structures is essential to understand brain function, to predict functional deficits following lesion or trauma, and to interpret radiological images. To improve learning of deep cerebral structures for novice neuroanatomists, a stereoscopic, rotatable view of a human brain was used to create a virtual brain that can be rotated in an easy-to-use web-based module: Neuronline. Learners can also slice the virtual brain in both the coronal and horizontal planes, allowing for the identification of deep brain structures. Each brain slice is matched to a corresponding MRI image and labels can be toggled on or off. An orientation diagram helps students locate a structure within the virtual brain. This tool is designed for learners with limited neuroanatomy experience. For the student new to neuroanatomy, learning the organization of deep brain structures and fiber tracts can be daunting. Neuronline also has the advantage of being portable, and can be used prior to gross anatomy lab sessions or in-lab as a study guide.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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