Bibliographic record
Abstract
There seems to be a growing tendency for scientific papers to become bifurcated, with more detailed material hidden away as “supplementary information.” As a reader and a reviewer, I have often found this arrangement awkward or even frustrating; the advertised supplementary information is sometimes hard to locate, and there is no standardized way of accessing it. The trend may have started in journals such as Science and Nature, which have a page limit. But in an era where increasingly few papers are read in print format, and where electronic storage is so cheap, both page limits and supplementary information seem hardly necessary. The traditional way of dealing with more detailed material is to add an appendix at the end of the text, following the references. If the word “appendix” appears archaic and “supplementary information” sounds more trendy, it could be labeled as such. The main idea is to keep all the material together and readily accessible, and to ensure that nothing is lost when the article is archived. Naturally there will be exceptions, such as video files or large files containing metadata. In some branches of science, this is rarely an issue. But where videos are required, they could be accessed via a URL link in the main article. A standard digital format would help to ensure accessibility for future generations of microscopists.
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 | 0.039 | 0.460 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.479 | 0.083 |
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".