Application‐based guidelines for best practices in plant flow cytometry
Bibliographic record
Abstract
Flow cytometry (FCM) is currently the most widely-used method to establish nuclear DNA content in plants. Since simple, 1-3-parameter, flow cytometers, which are sufficient for most plant applications, are commercially available at a reasonable price, the number of laboratories equipped with these instruments, and consequently new FCM users, has greatly increased over the last decade. This paper meets an urgent need for comprehensive recommendations for best practices in FCM for different plant science applications. We discuss advantages and limitations of establishing plant ploidy, genome size, DNA base composition, cell cycle activity, and level of endoreduplication. Applications of such measurements in plant systematics, ecology, molecular biology research, reproduction biology, tissue cultures, plant breeding, and seed sciences are described. Advice is included on how to obtain accurate and reliable results, as well as how to manage troubleshooting that may occur during sample preparation, cytometric measurements, and data handling. Each section is followed by best practice recommendations; tips as to what specific information should be provided in FCM papers are also provided.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.033 |
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".