CJN volume 40 issue 1 Cover and Back matter
Bibliographic record
Abstract
Articles with structured abstracts should be submitted under conventional headings of introduction, methods and materials, results, discussion, but other headings will be considered if more suitable. Figures Ideal resolution!Minimum resolution• Figures/Images in TIF, EPS, PDF, or JPG formats (must follow high resolution formats below) • Line Bitmap 1200 dpi (ideal) 600 dpi (min) • Color photo CMYK 300 dpi (ideal) 200 dpi (min) • B/W halftone (black and white photo) Grayscale 300 dpi (ideal) 200 dpi (min) • Line/halftone Grayscale 600 dpi (ideal) 200 dpi (min) Tables • Tables accepted in DOC format only.• Type tables double-spaced on pages separate from the text.• Provide a table number and title for each.• Particular care should be taken in the preparation of tables to ensure that the data are presented clearly and concisely.• Each column should have a short or abbreviated heading.• Place explanatory matter in footnotes, not in the heading.• Do not submit tables as photographs.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.835 | 0.774 |
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".