In vivo Population Averaged Stereotaxic T2w MRI Brain Template for the Adult Yucatan Micropig
Bibliographic record
Abstract
Population averaged brain templates are an essential tool for imaging-based neuroscience research, providing investigators with information about the expected size and morphology of brain structures and the spatial relationships between them, within a demographic cross-section. This allows for a standardized comparison of neuroimaging data between subjects and provides neuroimaging software with a probabilistic framework upon which further processing and analysis can be based. Many different templates have been created to represent specific study populations and made publicly available for human and animal research. An increasingly studied animal model in the neurosciences that still lacks appropriate brain templates is the adult Yucatan micropig. In particular, T2-weighted templates are absent in this species as a whole. To address this need and provide a tool for neuroscientists wishing to pursue neuroimaging research in the adult micropig, we present the construction of population averaged (n=16) T2-weighted MRI brain template for the adult Yucatan micropig. Additionally, we present initial analysis of T1-weighted (n=3), and diffusion-weighted (n=3) images through multimodal registration of these contrasts to our T2 template. The strategies used here may also be generalized to create similar templates for other study populations or species in need of template construction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".