Corrigendum: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics
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
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.”
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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