Corrigendum: Prevalence of increases in functional connectivity in visual, somatosensory and language areas in congenital blindness
Bibliographic record
Abstract
We would like to add one person to the list of authors for our manuscript. The new author list looks as follows:Heine L, Bahri MA, Cavaliere C, Soddu A, Reislev NL, Laureys S, Ptito M and Kupers RWe also add an explanation for the author contributions (see below):Author contributions: Ron Kupers and Maurice Ptito designed the study. Nina Reislev recruited participants, collected psychometric and brain imaging data. Lizette Heine, Mohamed Bahri, Carlo Cavaliere and Andrea Soddu analyzed the rsfMRI data. Lizette Heine, Ron Kupers and Maurice Ptito wrote the manuscript. Steven Laureys and Andrea Soddu edited the manuscript and provided scientific expertise. All the other files remain unchanged. We would like to ask to remove the name of Dr. Reislev from the Acknowledgment section as she has now been added to the list of authors. You can find the new Acknowledgment statement below.
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 | 0.003 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.036 |
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".