Interlaboratory Performance Study of Cyanobacteria <scp>DNA</scp> Reference Materials Using a <scp>qPCR</scp> Format for Monitoring Cyanobacterial Blooms
Bibliographic record
Abstract
ABSTRACT Digital PCR (dPCR) has increasingly been used as a primary measurement method for the characterization of nucleic acid reference materials. Nucleic acid reference materials are particularly useful when used for the validation and calibration of quantitative PCR (qPCR). In this study, we describe the development and characterization of Cyanobacteria DNA reference materials (RM) using dPCR. An international interlaboratory study involving 14 laboratories was conducted using the Cyanobacteria DNA RM in combination with a lyophilized PCR reagent designed for the monitoring of Cyanobacteria bloom events. Of the 55 scored study results obtained using qPCR‐based techniques, 62% were within the 8% relative expanded uncertainty based on dPCR measurements, while 100% of the study results returned satisfactory z scores calculated using a set performance coefficient of variation equivalent to one Ct value. The study participants' results indicate that the cyanobacteria DNA RM is fit for the purpose of method validation and quality control of the qPCR format used for monitoring toxic cyanobacteria algae bloom events. Most importantly, the study results demonstrated that the use of standardized reagents combined with highly characterized nucleic acid RMs allows qPCR‐based DNA quantification technology to reach levels of accuracy and reproducibility comparable to those achieved with digital PCR technology.
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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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".