(LivingPreprint) Representation in Brain Imaging Research: A Quebec demographic overview
Bibliographic record
Abstract
About NeuroLibre Living Preprint built at this reference repository/commit by roboneuro, based on the latest change by the author. ❤️ Living preprint: https://preprint.neurolibre.org/10.55458/neurolibre.00035 For the living preprints in JupyterBook format You can simply decompress (extract) the zip file and open index.html in your browser. For the living preprints in MyST format If you see the following folders after extracting the zip file, it means that the preprint is in MyST format: site execute html templates When you open the html/index.html file, you will be able to see the preprint content, however the static webpage components will not be properly loaded. This is because the static HTML assets were built with a base URL following the DOI format. To render your preprint properly, you can simply run the serve_preprint.py python script that is included in the archive: --- cd /LivingPreprint_10.55458_neurolibre_NeuroLibre_00035_a2059d python serve_preprint.py --- Then you can open the given URL in your browser. The preprint should look like its published version! Note: The site folder contains the living preprint as structured data (in json format), which is being used by NeuroLibre to serve your publication as a dynamic webpage. For more details, please visit the corresponding myst documentation. For details, please visit the corresponding NeuroLibre technical screening. https://neurolibre.org ✉️ info@neurolibre.org
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.179 | 0.063 |
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".