Corrigendum: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics
Bibliographic record
Abstract
A General Commentary on GRETNA: a graph theoretical network analysis toolbox for imaging Connectomics by Wang J, Wang X, Xia M, Liao X, Evans A and He Y (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front. Hum. Neurosci. 9:386. doi: 10.3389/fnhum.2015.00386Here, we would like to correct the two points as follows.1) The updated Table 1: Table 1. Summary of neuroscience connectomics tools 2) Discussion: “Specifically, compared with the recent developed GraphVar (Kruschwitz et al., 2015), GRETNA has distinct features in parallel computing, capability to preprocess R-fMRI data.” We would like to further clarify the description as “Specifically, compared with the recent developed GraphVar (Kruschwitz et al., 2015), GRETNA has distinct features in parallel computing. The GraphVar (beta v0.611) can assign several jobs to different CPUs by calling Matlab's parallel computing toolbox. The GRETNA can assign parallel tasks by calling the PSOM toolbox (Bellec et al., 2012), which helps GRETNA to record and manage the data generated during fMRI preprocessing or graph-based network analyses and to restart the pipeline from the failure steps.”
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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 | 0.006 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.073 | 0.065 |
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".