CYTO Lab Hacks: A platform for the exchange of innovations in cytometry
Bibliographic record
Abstract
This article reports on a conference workshop conducted at CYTO 2019. This workshop centered on an online directory for non-commercial cytometry innovations called CYTO Lab Hacks. The CYTO Lab Hacks website is being developed to become a curated platform to collate and to promote cytometry related materials developed by the wider scientific community. The website will present brief summaries and links to repositories with experimental protocols, descriptions of hardware changes, document templates, software code, and other innovations. The workshop outcomes, summarized in this manuscript, cover the topics of the website functionality and user experience, organization of the volunteer task force, and understanding the needs of the cytometry community in respect to sharing innovations.
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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 | 0.031 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.091 | 0.051 |
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".