Bibliographic record
Abstract
This issue opens the fifth volume of Neuroinformatics, which is a good time to look at how the journal is doing, as it has evolved quite a bit as I wrote a similar editorial for the second volume (De Schutter, 2004). What has not changed is that we are very proud about our editorial work. Our impact factor is excel-lent for a journal with a strong emphasis on informatics and methods, we started at 3.0 for 2004 and are now at 3.9. This puts us heads and shoulders above all computational neuro-science, machine learning, and neuroscience methods ’ journals. We rank in the top-half of neuroscience journals, better than many classic neuroscience titles, and do even better in infor-matics in which we are ranked fourth in inter-disciplinary computer science. This high impact factor is supported by two trends, a positive and a negative one. Rather negative is that we publish relatively few arti-cles, in fact, the third and fourth volumes con-tained a quarter less articles than the first two. This helps of course with the impact factor but also reflects a rather low article submission rate. We expect that the good impact factor will help to solve this problem but will also make sure that a higher influx of manuscripts will not lead to a lowering of the quality of the journal. Nevertheless, this volume will still include only four issues, the increase to six volumes has been postponed till we get a permanent increase in article submission. The positive trend is that our high impact factor is supported by the multidisciplinary nature of the journal. In fact, the current issue is quite representative for the majority of our articles: we have three articles fitting within
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.791 | 0.786 |
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".