How we are building Neuroanatomy and Behaviour for rigorous andopen science
Bibliographic record
Abstract
Neuroanatomy and Behaviour was founded to be a journal for rigorous and open science. In 2021, all of the empirical papers published engaged in at least one open science practice, such as open data or open protocols. The papers published have been carefully reviewed by two experts, but may also be sent to additional specialist reviewers for specific tasks, such as checking references or statistical approaches. In 2021, Neuroanatomy and Behaviour reached a key milestone and was accepted into the Directory of Open Access Journals, the world’s leading database of trustworthy open access journals. As we look towards 2022, we will continue improving our publication processes and working to share quality neuroscience without financial barriers for authors or readers.
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 | 0.351 | 0.444 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.012 | 0.105 |
| Scholarly communication | 0.046 | 0.076 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".