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Bibliographic record
Abstract
We welcome the following distinguished neurologists to the Neurology Editorial Board effective January 1, 2006: Gregory Cascino, MD, Rochester, MN; Murray Grossman, MD, Philadelphia, PA; Michael Hill, MD, Calgary, Alberta, Canada; Steven R. Levine, MD, New York, NY; Hanns Lochmuller, MD, Munich, Germany; Ruth Nass, MD, New York, NY; and Samuel Wiebe, MD, Calgary, Alberta, Canada. We thank our retiring Editorial Board members who have devoted enormous time and energy to Neurology and have helped to make the Journal the premier source of information in clinical neuroscience. Retiring Editorial Board members include Harry Chugani, MD; Thomas Feasby, MD; J. Timothy Greenamyre, MD, PhD; S. Claiborne Johnston, MD, PhD; Marc Patterson, MD; and William Powers, MD. The number of manuscripts we receive continues to increase every year. Neurology received 2,520 papers from January to August 2005. In 2004, we received 2,373 in that same time period. We are on a pace of receiving over 4,100 manuscripts this year. International submissions continue to constitute 65–70% of submissions. Web access of www.neurology.org has also increased; compared to September 2004, access to abstracts has risen 42%, access to full-text …
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 | 0.014 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.297 | 0.307 |
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".