Bibliographic record
Abstract
The first part of the title to this commentary, or a variation thereof, is commonplace in most published articles these days.If one were to look back historically at when the phenomenon of "supplemental information" began, it appears that it was sporadically introduced around the turn of the century about a decade ago.At that time, only a few journals adopted the idea of including additional information with the online version of an article.Indeed, the inclusion of supplemental information with published articles seems to correlate with the increased dependence of accessing a journal online.But if one were to ask the questions: who started this phenomenon and are there uniform guidelines as to what and how much should be included in this online inclusion, the answers are not readily apparent.In fact, the answers may lie in the answer to the question: why do we have supplemental information to begin with?An informal survey of editors of some high-impact journals indicates that the impetus for supplemental information came from the editorial boards and not from submitting authors.That is, authors were not looking for a way to include more information but editors wanted to see this, or at least a redistribution of the material they perceived as relevant.Many journals, for financial considerations, restrict word or character counts on their articles.In an effort to allow authors to maximize the data they are presenting, many journals allow detailed experimental procedures to be included in a supplemental information file.One editor of a prominent journal suggested that much of the material in the supplemental information is the result of author rebuttals to reviewers' comments or questions.Since there is increased online access, and supplemental information is never found in print-form, this represents a means of increasing transparency by showing all readers (or those who choose to access the online version) the responses of the authors.In addition, in data-rich fields such as genomics and proteomics, including all of the raw data in the main body of an article is simply not possible and such data is often included in supplemental information.Finally, some authors include still-shots of live-cell imaging in the articles and, in the interests of both transparency and completeness, include the full movie in supplemental information.These are certainly justifiable uses of this online section of an article.However, over the past few years it seems that supplemental information has evolved other uses, particularly by authors and journal reviewers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.071 | 0.054 |
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; both teacher heads 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".