Bibliographic record
Abstract
As we enter 2025, the importance of improving the level of understanding in the world is ever more apparent: understanding in the sense of increasing and communicating the sum of human knowledge, the main purpose of scientific journals; and understanding in the sense of acknowledging differences in views.For Open Biology, this means fair and balanced editorial decisions based on the opinions of both peer reviewers and the authors.Our editors are clearly managing this balancing act-compared with the last quarter, submissions to Open Biology were up and times to decision were down, and we are proud to say that we have many returning authors-but we wish to go further to enrich our authors' experience.This year we will be enhancing the communication between editors and authors on how to revise papers with the aim to formulate a mutually agreed structured revision plan.In this way, we will augment the journal's reputation for clear and constructive peer review.Constructive peer review is the core mission of Open Biology, and we have introduced a new feature to showcase this.We offer our reviewers the opportunity to write a 'Spotlight' commentary on research that they have evaluated that is particularly noteworthy for both the rigour of the science and its significance.Our editors can also select noteworthy contributions for highlighting in our Cassyni Research Seminars series that has its own dedicated channel.We continue to build the journal as a place for discussion and debate.Our Open Questions articles highlight advances in an area of cellular and molecular biology that is developing quickly and ripe for discovery-many thanks to our Associate Editors Martha Cyert and Tin Tin Su for advocating for this initiative-and the winner of the first Open Questions competition will be announced shortly.We received 26 submissions to the competition and were so impressed by the quality of the articles that we intend to run the competition every year.
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 | 0.013 | 0.141 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.097 | 0.147 |
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".