Bibliographic record
Abstract
The inclusion of quantitative methods to supplement, enhance, and even guide experimental inquiry has become imperative in biological research. With advances in techniques and technology, biological studies produce large amounts of unique data that can be understood only through the collaborative efforts of skilled data scientists and biologists themselves. As with any collaboration, the success rate of the collaborating groups is contingent on the ability of all parties to effectively communicate ideas and results. Using R at the Bench: Step-by-Step Data Analytics for Biologists, by Martina Bremer and Rebecca W. Doerge, is a resource that helps facilitate that communication process. It is not only a guidebook for biologists looking to strengthen their data-analytics understanding but also a broad reference to biology-specific scenarios that can benefit statisticians and data scientists alike. Bremer and Doerge convey an important message within their short—but necessary—second chapter describing the common pitfalls with statistical analysis in biology. The placement of this message near the front of their text was essential and reminded me why a comprehensive guidebook such as theirs has value to our quantitative biology community. Many of the ideas, such as the list of common mistakes or sources of variation, will come off as obvious to an experienced quantitative biologist or statistician. I find, however, that these apparently obvious ideas are often the most difficult to articulate, so having them listed in a concise manner with well-placed examples will provide me an excellent resource to tap into prior to conversations with collaborators.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.007 | 0.025 |
| Insufficient payload (model declined to judge) | 0.058 | 0.078 |
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".