Bibliographic record
Abstract
be included in the manuscript.If the title is long, supply also a shortened form of the title not exceeding 40 characters, including spaces.Addresses should be shown under the authors name, including e-mail address if available.2. Main headings.Main headings should be numbered, centred and shown thus: Preliminary results3. Theorems.The titles LEMMA, THEOREM, COROLLARY, REMARK, DEFINITION etc. should be left-justified and numbered consecutively with arabic numerals, e.g.LEMMA 1.1.The content of the lemma, theorem etc. should follow, as here. Acknowledgements.If acknowledgements of support and assistance are made, these should be given at the end of the article.Footnotes should be avoided. Equations.Equations should be punctuated to conform to their place in the syntax of the sentence.Equation numbers should be shown on the right in round brackets. References.The reference list should be in ALPHABETICAL ORDER by name of first author, preceded by a reference number in square brackets.These references should be cited in the text by giving the appropriate number in square brackets.The following layout for books, journal articles, theses, articles in books, and conference proceedings respectively, must be followed.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.851 | 0.848 |
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".