How has structuring your ideas into protocols contributed to your research progress and facilitated collaboration with others in the field?
Bibliographic record
Abstract
Stuck in the middle of a project or degree and unable to see an immediate path forward is a common experience for researchers. In this backstory, Xi shares how tidying up a step-by-step protocol for STAR Protocols has led to unforeseen progress and connections. He has collaborated with the STAR Protocols team to publish five protocols and review three manuscripts. He finds that structuring thoughts in protocol form helps build confidence and explore ideas behind the data, especially when outcomes are unexpected. Additionally, the troubleshooting process can unravel interesting biological questions. For more information on the protocols related to this backstory, please refer to Zhang et al. 1 , 2 , 3
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 | 0.173 | 0.386 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.029 | 0.040 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.023 |
| Insufficient payload (model declined to judge) | 0.029 | 0.029 |
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".