Editorial Moving Education Forward, Again!
Bibliographic record
Abstract
Educating biologists and computational biologists in methods and analyses is an ever-growing challenge. The amount of data and tools available to the scientific community continue to grow, and with that, there is a growing need to teach how to get the most out of this information. The PLOS Computational Biology Education section was launched in January 2006 [1] to address this challenge, with the first contribution published in April of that year [2]. In these past eight years, we have published more than 50 Education articles on different topics and have started two specialized Education collections: ‘‘Bioin-formatics: Starting Early’ ’ [3], which addresses the needs of teaching bioinfor-matics at the secondary school level, and the first PLOS online textbook, ‘‘Transla-tional Bioinformatics’ ’ [4]. After eight years in the role of editor of this section, I have decided that it is time to step aside to allow fresh ideas to be introduced into building the Education section and new directions to be followed as our science continues to mature. I am delighted that Francis Ouellette (Ontario
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.046 | 0.024 |
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".