The γδ T Cell Club: bringing a global scientific community together in the age of Zoom
Bibliographic record
Abstract
Immunologists are very social people-they love to meet other immunologists and talk about immunology (and immunologists). Constantly! γδ T-cell researchers are no exception. On the contrary, as there are not so many of them compared to, say, researchers working on dendritic cells, they especially crave frequent interactions with like-minded scientists. This is where the technological solutions being developed during the coronavirus disease 2019 (COVID-19) pandemic come into play that have, almost overnight, allowed researchers to hold meetings and lectures online. We here describe how we set up the virtual 'γδ T Cell Club', a monthly webinar series that aims to bring the field closer together, and present our musings about what we have learned from this experience, which we hope is useful for other researchers interested in connecting online.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".