Ten simple rules for writing a PLOS Computational Biology quick tips article
Bibliographic record
Abstract
At that time, the change in how we communicate sciences had already begun [2]: social media was blooming, the information deluge was ongoing, and the fear of missing information was anchored in each of us.The Ten Simple Rules collection filled a space in scientific publications where researchers eager to share their experiences, wisdom, and doubts could quickly do it using a colloquial narrative.The topics covered were broad themes in scientific practice, such as soft skills and career development, captured in a concise and quick-to-read format, something between a blog post and a scientific article.This type of article attracted much interest from readers.In 2018, the collection reached the milestone of 1,000 Rules [3], and today this figure is above 250 papers (2,500 Rules!) [4].With the increase of technical and scientific topics, in 2013, PLOS Computational Biology tried a new experience with a similar format-we introduced "Quick Tips" (QT) articles-with the attempt to make a clear distinction between the more specific and focused scientific activities and skills presented with resources, databases, and other tools in Quick Tips versus the broader themes presented in a Ten Simple Rules article."Ten Simple Rules for Writing a PLOS Ten Simple Rules Article" [5] explains the Ten Simple Rules concept, format, and reasoning very well and is still relevant today.Inspired by that article, we wrote this Ten Simple Rules paper intending to accomplish a similar task: explaining to our community what a Quick Tips article is about and how a Quick Tips article differs from a Ten Simple Rules article.The authors are the Section Editors of the Education Collection (PP and BFFO), which encompasses the Quick Tips, and the Section Editors of the Ten Simple Rules Collection (RS and SM).In our work at PLOS CB, we are routinely deliberating the merits of a submission being a Ten Simple Rules or a Quick Tips, and for this reason, we decided to put these Ten Simple Rules about Quick Tips together.Historically, several papers were submitted as Ten Simple Rules that should have been Quick Tips.Still, we will refrain from commiserating about things not done but rather present what we hope will be a clear distinction that will make it obvious in the future why articles are best considered as Quick Tips versus Ten Simple Rules (or vice-versa).We hope and plan for this present article to be helpful for future writers willing to contribute to this collection and to help clarify the differences between Ten Simple Rules and Quick Tips.Why is this not a Quick Tips article but a Ten Simple Rules article, and why are we not as endearing as Dashnow, Lonsdale, and Bourne were in doing a "Ten Quick Tips for Writing a PLOS Quick Tips Article"?The reason is simple.
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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 | 0.065 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.032 | 0.025 |
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".