An open access resource for marmoset neuroscientific apparatus
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
) for neuroscientific inquiry has grown precipitously over the past two decades. Despite windfalls of grant support from funding initiatives in North America, Europe, and Asia to model human brain diseases in the marmoset, marmoset-specific apparatus are of sparse availability from commercial vendors and thus are often developed and reside within individual laboratories. Through our collective research efforts, we have designed and vetted myriad designs for awake or anesthetized magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), as well as focused ultrasound (FUS), electrophysiology, optical imaging, surgery, and behavior in marmosets across the age-span. This resource makes these designs openly available, reducing the burden of de novo development across the marmoset field. The computer-aided-design (CAD) files are publicly available through the Marmoset Brain Connectome (MBC) resource (https://www.marmosetbrainconnectome.org/apparatus/) and include dozens of downloadable CAD assemblies, software and online calculators for marmoset neuroscience. In addition, we make available a variety of vetted touchscreen and task-based fMRI code and stimuli. Here, we highlight the online interface and the development and validation of a few yet unpublished resources: software to automatically extract the head morphology of a marmoset from a CT and produce a 3D printable helmet for awake neuroimaging, and the design and validation of 8-channel and 14-channel receive arrays for imaging deep structures during anatomical and functional MRI.
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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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