Collection of transformations between Human Brain standard spaces, 2018 version
Bibliographic record
Abstract
This dataset contains deformation fields that provide a non-linear mapping between the four human template spaces that are at the core of the HBP EBRAINS platform: –MNI ICBM152 nonlinear 2009c asymmetric [http://nist.mni.mcgill.ca/?p=904](http://nist.mni.mcgill.ca/?p=904); –MNI Colin27 [http://nist.mni.mcgill.ca/?p=935](http://nist.mni.mcgill.ca/?p=935); –BigBrain, 2015 release, in native histological space [https://doi.org/10.1126%2Fscience.1235381](https://doi.org/10.1126%2Fscience.1235381); –Infant template [https://doi.org/10.25493%2F49QZ-AWZ](https://doi.org/10.25493%2F49QZ-AWZ). The cross-template transformations are diffeomorphisms, which are computed based on the alignment of the folding pattern across the different brains (DISCO method) and maximization of the grey–white matter segmentation overlap (DARTEL).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.123 |
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".