neurodata/ndmg: Stable ndmg-DWI Pipeline Release
Bibliographic record
Abstract
Main Package Features One-click pipeline for structural connectome estimation from DWI and T1w images Leverages Dipy, Plotly, Nilearn, FSL, Networkx, and others Supports several command-line APIs, including the BIDS app specification Has session- and group-level analysis, performing connectome estimation and summary statistic computation, respectively Session-level analysis generates 1mm resolution connectomes across 25 parcellations ranging in size from 48 to 72,000 nodes, all in MNI152 standard space. Direct interfaces with Amazon Web Services for batch processing of data stored in the cloud. Public repository of connectomes available at http://m2g.io For more information, please see the README of this repository. Install Instructions pip install ndmg* * once FSL is installed OR Available through Docker, with: docker pull bids/ndmg:v0.1.0. Container is compatible with Singularity 2.4.1, though pulling from Singularity Hub is not currently available for this pipeline. Usage Instructions Through the BIDS app interface, ndmg can be used as follows: ndmg_bids bids_directory output_directory {session, group} [--participant_level []] [--session_level []]
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.315 | 0.358 |
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".